Google Native

Pulumi Official
Package maintained by Pulumi
v0.19.0 published on Thursday, May 19, 2022 by Pulumi

Autoscaler

Creates an autoscaler in the specified project using the data included in the request.

Create a Autoscaler Resource

new Autoscaler(name: string, args?: AutoscalerArgs, opts?: CustomResourceOptions);
@overload
def Autoscaler(resource_name: str,
               opts: Optional[ResourceOptions] = None,
               autoscaling_policy: Optional[AutoscalingPolicyArgs] = None,
               description: Optional[str] = None,
               name: Optional[str] = None,
               project: Optional[str] = None,
               request_id: Optional[str] = None,
               target: Optional[str] = None,
               zone: Optional[str] = None)
@overload
def Autoscaler(resource_name: str,
               args: Optional[AutoscalerArgs] = None,
               opts: Optional[ResourceOptions] = None)
func NewAutoscaler(ctx *Context, name string, args *AutoscalerArgs, opts ...ResourceOption) (*Autoscaler, error)
public Autoscaler(string name, AutoscalerArgs? args = null, CustomResourceOptions? opts = null)
public Autoscaler(String name, AutoscalerArgs args)
public Autoscaler(String name, AutoscalerArgs args, CustomResourceOptions options)
type: google-native:compute/alpha:Autoscaler
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

name string
The unique name of the resource.
args AutoscalerArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
resource_name str
The unique name of the resource.
args AutoscalerArgs
The arguments to resource properties.
opts ResourceOptions
Bag of options to control resource's behavior.
ctx Context
Context object for the current deployment.
name string
The unique name of the resource.
args AutoscalerArgs
The arguments to resource properties.
opts ResourceOption
Bag of options to control resource's behavior.
name string
The unique name of the resource.
args AutoscalerArgs
The arguments to resource properties.
opts CustomResourceOptions
Bag of options to control resource's behavior.
name String
The unique name of the resource.
args AutoscalerArgs
The arguments to resource properties.
options CustomResourceOptions
Bag of options to control resource's behavior.

Autoscaler Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

The Autoscaler resource accepts the following input properties:

AutoscalingPolicy Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyArgs

The configuration parameters for the autoscaling algorithm. You can define one or more signals for an autoscaler: cpuUtilization, customMetricUtilizations, and loadBalancingUtilization. If none of these are specified, the default will be to autoscale based on cpuUtilization to 0.6 or 60%.

Description string

An optional description of this resource. Provide this property when you create the resource.

Name string

Name of the resource. Provided by the client when the resource is created. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash.

Project string
RequestId string

An optional request ID to identify requests. Specify a unique request ID so that if you must retry your request, the server will know to ignore the request if it has already been completed. For example, consider a situation where you make an initial request and the request times out. If you make the request again with the same request ID, the server can check if original operation with the same request ID was received, and if so, will ignore the second request. This prevents clients from accidentally creating duplicate commitments. The request ID must be a valid UUID with the exception that zero UUID is not supported ( 00000000-0000-0000-0000-000000000000).

Target string

URL of the managed instance group that this autoscaler will scale. This field is required when creating an autoscaler.

Zone string
AutoscalingPolicy AutoscalingPolicyArgs

The configuration parameters for the autoscaling algorithm. You can define one or more signals for an autoscaler: cpuUtilization, customMetricUtilizations, and loadBalancingUtilization. If none of these are specified, the default will be to autoscale based on cpuUtilization to 0.6 or 60%.

Description string

An optional description of this resource. Provide this property when you create the resource.

Name string

Name of the resource. Provided by the client when the resource is created. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash.

Project string
RequestId string

An optional request ID to identify requests. Specify a unique request ID so that if you must retry your request, the server will know to ignore the request if it has already been completed. For example, consider a situation where you make an initial request and the request times out. If you make the request again with the same request ID, the server can check if original operation with the same request ID was received, and if so, will ignore the second request. This prevents clients from accidentally creating duplicate commitments. The request ID must be a valid UUID with the exception that zero UUID is not supported ( 00000000-0000-0000-0000-000000000000).

Target string

URL of the managed instance group that this autoscaler will scale. This field is required when creating an autoscaler.

Zone string
autoscalingPolicy AutoscalingPolicyArgs

The configuration parameters for the autoscaling algorithm. You can define one or more signals for an autoscaler: cpuUtilization, customMetricUtilizations, and loadBalancingUtilization. If none of these are specified, the default will be to autoscale based on cpuUtilization to 0.6 or 60%.

description String

An optional description of this resource. Provide this property when you create the resource.

name String

Name of the resource. Provided by the client when the resource is created. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash.

project String
requestId String

An optional request ID to identify requests. Specify a unique request ID so that if you must retry your request, the server will know to ignore the request if it has already been completed. For example, consider a situation where you make an initial request and the request times out. If you make the request again with the same request ID, the server can check if original operation with the same request ID was received, and if so, will ignore the second request. This prevents clients from accidentally creating duplicate commitments. The request ID must be a valid UUID with the exception that zero UUID is not supported ( 00000000-0000-0000-0000-000000000000).

target String

URL of the managed instance group that this autoscaler will scale. This field is required when creating an autoscaler.

zone String
autoscalingPolicy AutoscalingPolicyArgs

The configuration parameters for the autoscaling algorithm. You can define one or more signals for an autoscaler: cpuUtilization, customMetricUtilizations, and loadBalancingUtilization. If none of these are specified, the default will be to autoscale based on cpuUtilization to 0.6 or 60%.

description string

An optional description of this resource. Provide this property when you create the resource.

name string

Name of the resource. Provided by the client when the resource is created. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash.

project string
requestId string

An optional request ID to identify requests. Specify a unique request ID so that if you must retry your request, the server will know to ignore the request if it has already been completed. For example, consider a situation where you make an initial request and the request times out. If you make the request again with the same request ID, the server can check if original operation with the same request ID was received, and if so, will ignore the second request. This prevents clients from accidentally creating duplicate commitments. The request ID must be a valid UUID with the exception that zero UUID is not supported ( 00000000-0000-0000-0000-000000000000).

target string

URL of the managed instance group that this autoscaler will scale. This field is required when creating an autoscaler.

zone string
autoscaling_policy AutoscalingPolicyArgs

The configuration parameters for the autoscaling algorithm. You can define one or more signals for an autoscaler: cpuUtilization, customMetricUtilizations, and loadBalancingUtilization. If none of these are specified, the default will be to autoscale based on cpuUtilization to 0.6 or 60%.

description str

An optional description of this resource. Provide this property when you create the resource.

name str

Name of the resource. Provided by the client when the resource is created. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash.

project str
request_id str

An optional request ID to identify requests. Specify a unique request ID so that if you must retry your request, the server will know to ignore the request if it has already been completed. For example, consider a situation where you make an initial request and the request times out. If you make the request again with the same request ID, the server can check if original operation with the same request ID was received, and if so, will ignore the second request. This prevents clients from accidentally creating duplicate commitments. The request ID must be a valid UUID with the exception that zero UUID is not supported ( 00000000-0000-0000-0000-000000000000).

target str

URL of the managed instance group that this autoscaler will scale. This field is required when creating an autoscaler.

zone str
autoscalingPolicy Property Map

The configuration parameters for the autoscaling algorithm. You can define one or more signals for an autoscaler: cpuUtilization, customMetricUtilizations, and loadBalancingUtilization. If none of these are specified, the default will be to autoscale based on cpuUtilization to 0.6 or 60%.

description String

An optional description of this resource. Provide this property when you create the resource.

name String

Name of the resource. Provided by the client when the resource is created. The name must be 1-63 characters long, and comply with RFC1035. Specifically, the name must be 1-63 characters long and match the regular expression [a-z]([-a-z0-9]*[a-z0-9])? which means the first character must be a lowercase letter, and all following characters must be a dash, lowercase letter, or digit, except the last character, which cannot be a dash.

project String
requestId String

An optional request ID to identify requests. Specify a unique request ID so that if you must retry your request, the server will know to ignore the request if it has already been completed. For example, consider a situation where you make an initial request and the request times out. If you make the request again with the same request ID, the server can check if original operation with the same request ID was received, and if so, will ignore the second request. This prevents clients from accidentally creating duplicate commitments. The request ID must be a valid UUID with the exception that zero UUID is not supported ( 00000000-0000-0000-0000-000000000000).

target String

URL of the managed instance group that this autoscaler will scale. This field is required when creating an autoscaler.

zone String

Outputs

All input properties are implicitly available as output properties. Additionally, the Autoscaler resource produces the following output properties:

CreationTimestamp string

Creation timestamp in RFC3339 text format.

Id string

The provider-assigned unique ID for this managed resource.

Kind string

Type of the resource. Always compute#autoscaler for autoscalers.

RecommendedSize int

Target recommended MIG size (number of instances) computed by autoscaler. Autoscaler calculates the recommended MIG size even when the autoscaling policy mode is different from ON. This field is empty when autoscaler is not connected to an existing managed instance group or autoscaler did not generate its prediction.

Region string

URL of the region where the instance group resides (for autoscalers living in regional scope).

ScalingScheduleStatus Dictionary<string, string>

Status information of existing scaling schedules.

SelfLink string

Server-defined URL for the resource.

SelfLinkWithId string

Server-defined URL for this resource with the resource id.

Status string

The status of the autoscaler configuration. Current set of possible values: - PENDING: Autoscaler backend hasn't read new/updated configuration. - DELETING: Configuration is being deleted. - ACTIVE: Configuration is acknowledged to be effective. Some warnings might be present in the statusDetails field. - ERROR: Configuration has errors. Actionable for users. Details are present in the statusDetails field. New values might be added in the future.

StatusDetails List<Pulumi.GoogleNative.Compute.Alpha.Outputs.AutoscalerStatusDetailsResponse>

Human-readable details about the current state of the autoscaler. Read the documentation for Commonly returned status messages for examples of status messages you might encounter.

CreationTimestamp string

Creation timestamp in RFC3339 text format.

Id string

The provider-assigned unique ID for this managed resource.

Kind string

Type of the resource. Always compute#autoscaler for autoscalers.

RecommendedSize int

Target recommended MIG size (number of instances) computed by autoscaler. Autoscaler calculates the recommended MIG size even when the autoscaling policy mode is different from ON. This field is empty when autoscaler is not connected to an existing managed instance group or autoscaler did not generate its prediction.

Region string

URL of the region where the instance group resides (for autoscalers living in regional scope).

ScalingScheduleStatus map[string]string

Status information of existing scaling schedules.

SelfLink string

Server-defined URL for the resource.

SelfLinkWithId string

Server-defined URL for this resource with the resource id.

Status string

The status of the autoscaler configuration. Current set of possible values: - PENDING: Autoscaler backend hasn't read new/updated configuration. - DELETING: Configuration is being deleted. - ACTIVE: Configuration is acknowledged to be effective. Some warnings might be present in the statusDetails field. - ERROR: Configuration has errors. Actionable for users. Details are present in the statusDetails field. New values might be added in the future.

StatusDetails []AutoscalerStatusDetailsResponse

Human-readable details about the current state of the autoscaler. Read the documentation for Commonly returned status messages for examples of status messages you might encounter.

creationTimestamp String

Creation timestamp in RFC3339 text format.

id String

The provider-assigned unique ID for this managed resource.

kind String

Type of the resource. Always compute#autoscaler for autoscalers.

recommendedSize Integer

Target recommended MIG size (number of instances) computed by autoscaler. Autoscaler calculates the recommended MIG size even when the autoscaling policy mode is different from ON. This field is empty when autoscaler is not connected to an existing managed instance group or autoscaler did not generate its prediction.

region String

URL of the region where the instance group resides (for autoscalers living in regional scope).

scalingScheduleStatus Map

Status information of existing scaling schedules.

selfLink String

Server-defined URL for the resource.

selfLinkWithId String

Server-defined URL for this resource with the resource id.

status String

The status of the autoscaler configuration. Current set of possible values: - PENDING: Autoscaler backend hasn't read new/updated configuration. - DELETING: Configuration is being deleted. - ACTIVE: Configuration is acknowledged to be effective. Some warnings might be present in the statusDetails field. - ERROR: Configuration has errors. Actionable for users. Details are present in the statusDetails field. New values might be added in the future.

statusDetails ListStatusDetailsResponse>

Human-readable details about the current state of the autoscaler. Read the documentation for Commonly returned status messages for examples of status messages you might encounter.

creationTimestamp string

Creation timestamp in RFC3339 text format.

id string

The provider-assigned unique ID for this managed resource.

kind string

Type of the resource. Always compute#autoscaler for autoscalers.

recommendedSize number

Target recommended MIG size (number of instances) computed by autoscaler. Autoscaler calculates the recommended MIG size even when the autoscaling policy mode is different from ON. This field is empty when autoscaler is not connected to an existing managed instance group or autoscaler did not generate its prediction.

region string

URL of the region where the instance group resides (for autoscalers living in regional scope).

scalingScheduleStatus {[key: string]: string}

Status information of existing scaling schedules.

selfLink string

Server-defined URL for the resource.

selfLinkWithId string

Server-defined URL for this resource with the resource id.

status string

The status of the autoscaler configuration. Current set of possible values: - PENDING: Autoscaler backend hasn't read new/updated configuration. - DELETING: Configuration is being deleted. - ACTIVE: Configuration is acknowledged to be effective. Some warnings might be present in the statusDetails field. - ERROR: Configuration has errors. Actionable for users. Details are present in the statusDetails field. New values might be added in the future.

statusDetails AutoscalerStatusDetailsResponse[]

Human-readable details about the current state of the autoscaler. Read the documentation for Commonly returned status messages for examples of status messages you might encounter.

creation_timestamp str

Creation timestamp in RFC3339 text format.

id str

The provider-assigned unique ID for this managed resource.

kind str

Type of the resource. Always compute#autoscaler for autoscalers.

recommended_size int

Target recommended MIG size (number of instances) computed by autoscaler. Autoscaler calculates the recommended MIG size even when the autoscaling policy mode is different from ON. This field is empty when autoscaler is not connected to an existing managed instance group or autoscaler did not generate its prediction.

region str

URL of the region where the instance group resides (for autoscalers living in regional scope).

scaling_schedule_status Mapping[str, str]

Status information of existing scaling schedules.

self_link str

Server-defined URL for the resource.

self_link_with_id str

Server-defined URL for this resource with the resource id.

status str

The status of the autoscaler configuration. Current set of possible values: - PENDING: Autoscaler backend hasn't read new/updated configuration. - DELETING: Configuration is being deleted. - ACTIVE: Configuration is acknowledged to be effective. Some warnings might be present in the statusDetails field. - ERROR: Configuration has errors. Actionable for users. Details are present in the statusDetails field. New values might be added in the future.

status_details Sequence[AutoscalerStatusDetailsResponse]

Human-readable details about the current state of the autoscaler. Read the documentation for Commonly returned status messages for examples of status messages you might encounter.

creationTimestamp String

Creation timestamp in RFC3339 text format.

id String

The provider-assigned unique ID for this managed resource.

kind String

Type of the resource. Always compute#autoscaler for autoscalers.

recommendedSize Number

Target recommended MIG size (number of instances) computed by autoscaler. Autoscaler calculates the recommended MIG size even when the autoscaling policy mode is different from ON. This field is empty when autoscaler is not connected to an existing managed instance group or autoscaler did not generate its prediction.

region String

URL of the region where the instance group resides (for autoscalers living in regional scope).

scalingScheduleStatus Map

Status information of existing scaling schedules.

selfLink String

Server-defined URL for the resource.

selfLinkWithId String

Server-defined URL for this resource with the resource id.

status String

The status of the autoscaler configuration. Current set of possible values: - PENDING: Autoscaler backend hasn't read new/updated configuration. - DELETING: Configuration is being deleted. - ACTIVE: Configuration is acknowledged to be effective. Some warnings might be present in the statusDetails field. - ERROR: Configuration has errors. Actionable for users. Details are present in the statusDetails field. New values might be added in the future.

statusDetails List

Human-readable details about the current state of the autoscaler. Read the documentation for Commonly returned status messages for examples of status messages you might encounter.

Supporting Types

AutoscalerStatusDetailsResponse

Message string

The status message.

Type string

The type of error, warning, or notice returned. Current set of possible values: - ALL_INSTANCES_UNHEALTHY (WARNING): All instances in the instance group are unhealthy (not in RUNNING state). - BACKEND_SERVICE_DOES_NOT_EXIST (ERROR): There is no backend service attached to the instance group. - CAPPED_AT_MAX_NUM_REPLICAS (WARNING): Autoscaler recommends a size greater than maxNumReplicas. - CUSTOM_METRIC_DATA_POINTS_TOO_SPARSE (WARNING): The custom metric samples are not exported often enough to be a credible base for autoscaling. - CUSTOM_METRIC_INVALID (ERROR): The custom metric that was specified does not exist or does not have the necessary labels. - MIN_EQUALS_MAX (WARNING): The minNumReplicas is equal to maxNumReplicas. This means the autoscaler cannot add or remove instances from the instance group. - MISSING_CUSTOM_METRIC_DATA_POINTS (WARNING): The autoscaler did not receive any data from the custom metric configured for autoscaling. - MISSING_LOAD_BALANCING_DATA_POINTS (WARNING): The autoscaler is configured to scale based on a load balancing signal but the instance group has not received any requests from the load balancer. - MODE_OFF (WARNING): Autoscaling is turned off. The number of instances in the group won't change automatically. The autoscaling configuration is preserved. - MODE_ONLY_UP (WARNING): Autoscaling is in the "Autoscale only out" mode. The autoscaler can add instances but not remove any. - MORE_THAN_ONE_BACKEND_SERVICE (ERROR): The instance group cannot be autoscaled because it has more than one backend service attached to it. - NOT_ENOUGH_QUOTA_AVAILABLE (ERROR): There is insufficient quota for the necessary resources, such as CPU or number of instances. - REGION_RESOURCE_STOCKOUT (ERROR): Shown only for regional autoscalers: there is a resource stockout in the chosen region. - SCALING_TARGET_DOES_NOT_EXIST (ERROR): The target to be scaled does not exist. - UNSUPPORTED_MAX_RATE_LOAD_BALANCING_CONFIGURATION (ERROR): Autoscaling does not work with an HTTP/S load balancer that has been configured for maxRate. - ZONE_RESOURCE_STOCKOUT (ERROR): For zonal autoscalers: there is a resource stockout in the chosen zone. For regional autoscalers: in at least one of the zones you're using there is a resource stockout. New values might be added in the future. Some of the values might not be available in all API versions.

Message string

The status message.

Type string

The type of error, warning, or notice returned. Current set of possible values: - ALL_INSTANCES_UNHEALTHY (WARNING): All instances in the instance group are unhealthy (not in RUNNING state). - BACKEND_SERVICE_DOES_NOT_EXIST (ERROR): There is no backend service attached to the instance group. - CAPPED_AT_MAX_NUM_REPLICAS (WARNING): Autoscaler recommends a size greater than maxNumReplicas. - CUSTOM_METRIC_DATA_POINTS_TOO_SPARSE (WARNING): The custom metric samples are not exported often enough to be a credible base for autoscaling. - CUSTOM_METRIC_INVALID (ERROR): The custom metric that was specified does not exist or does not have the necessary labels. - MIN_EQUALS_MAX (WARNING): The minNumReplicas is equal to maxNumReplicas. This means the autoscaler cannot add or remove instances from the instance group. - MISSING_CUSTOM_METRIC_DATA_POINTS (WARNING): The autoscaler did not receive any data from the custom metric configured for autoscaling. - MISSING_LOAD_BALANCING_DATA_POINTS (WARNING): The autoscaler is configured to scale based on a load balancing signal but the instance group has not received any requests from the load balancer. - MODE_OFF (WARNING): Autoscaling is turned off. The number of instances in the group won't change automatically. The autoscaling configuration is preserved. - MODE_ONLY_UP (WARNING): Autoscaling is in the "Autoscale only out" mode. The autoscaler can add instances but not remove any. - MORE_THAN_ONE_BACKEND_SERVICE (ERROR): The instance group cannot be autoscaled because it has more than one backend service attached to it. - NOT_ENOUGH_QUOTA_AVAILABLE (ERROR): There is insufficient quota for the necessary resources, such as CPU or number of instances. - REGION_RESOURCE_STOCKOUT (ERROR): Shown only for regional autoscalers: there is a resource stockout in the chosen region. - SCALING_TARGET_DOES_NOT_EXIST (ERROR): The target to be scaled does not exist. - UNSUPPORTED_MAX_RATE_LOAD_BALANCING_CONFIGURATION (ERROR): Autoscaling does not work with an HTTP/S load balancer that has been configured for maxRate. - ZONE_RESOURCE_STOCKOUT (ERROR): For zonal autoscalers: there is a resource stockout in the chosen zone. For regional autoscalers: in at least one of the zones you're using there is a resource stockout. New values might be added in the future. Some of the values might not be available in all API versions.

message String

The status message.

type String

The type of error, warning, or notice returned. Current set of possible values: - ALL_INSTANCES_UNHEALTHY (WARNING): All instances in the instance group are unhealthy (not in RUNNING state). - BACKEND_SERVICE_DOES_NOT_EXIST (ERROR): There is no backend service attached to the instance group. - CAPPED_AT_MAX_NUM_REPLICAS (WARNING): Autoscaler recommends a size greater than maxNumReplicas. - CUSTOM_METRIC_DATA_POINTS_TOO_SPARSE (WARNING): The custom metric samples are not exported often enough to be a credible base for autoscaling. - CUSTOM_METRIC_INVALID (ERROR): The custom metric that was specified does not exist or does not have the necessary labels. - MIN_EQUALS_MAX (WARNING): The minNumReplicas is equal to maxNumReplicas. This means the autoscaler cannot add or remove instances from the instance group. - MISSING_CUSTOM_METRIC_DATA_POINTS (WARNING): The autoscaler did not receive any data from the custom metric configured for autoscaling. - MISSING_LOAD_BALANCING_DATA_POINTS (WARNING): The autoscaler is configured to scale based on a load balancing signal but the instance group has not received any requests from the load balancer. - MODE_OFF (WARNING): Autoscaling is turned off. The number of instances in the group won't change automatically. The autoscaling configuration is preserved. - MODE_ONLY_UP (WARNING): Autoscaling is in the "Autoscale only out" mode. The autoscaler can add instances but not remove any. - MORE_THAN_ONE_BACKEND_SERVICE (ERROR): The instance group cannot be autoscaled because it has more than one backend service attached to it. - NOT_ENOUGH_QUOTA_AVAILABLE (ERROR): There is insufficient quota for the necessary resources, such as CPU or number of instances. - REGION_RESOURCE_STOCKOUT (ERROR): Shown only for regional autoscalers: there is a resource stockout in the chosen region. - SCALING_TARGET_DOES_NOT_EXIST (ERROR): The target to be scaled does not exist. - UNSUPPORTED_MAX_RATE_LOAD_BALANCING_CONFIGURATION (ERROR): Autoscaling does not work with an HTTP/S load balancer that has been configured for maxRate. - ZONE_RESOURCE_STOCKOUT (ERROR): For zonal autoscalers: there is a resource stockout in the chosen zone. For regional autoscalers: in at least one of the zones you're using there is a resource stockout. New values might be added in the future. Some of the values might not be available in all API versions.

message string

The status message.

type string

The type of error, warning, or notice returned. Current set of possible values: - ALL_INSTANCES_UNHEALTHY (WARNING): All instances in the instance group are unhealthy (not in RUNNING state). - BACKEND_SERVICE_DOES_NOT_EXIST (ERROR): There is no backend service attached to the instance group. - CAPPED_AT_MAX_NUM_REPLICAS (WARNING): Autoscaler recommends a size greater than maxNumReplicas. - CUSTOM_METRIC_DATA_POINTS_TOO_SPARSE (WARNING): The custom metric samples are not exported often enough to be a credible base for autoscaling. - CUSTOM_METRIC_INVALID (ERROR): The custom metric that was specified does not exist or does not have the necessary labels. - MIN_EQUALS_MAX (WARNING): The minNumReplicas is equal to maxNumReplicas. This means the autoscaler cannot add or remove instances from the instance group. - MISSING_CUSTOM_METRIC_DATA_POINTS (WARNING): The autoscaler did not receive any data from the custom metric configured for autoscaling. - MISSING_LOAD_BALANCING_DATA_POINTS (WARNING): The autoscaler is configured to scale based on a load balancing signal but the instance group has not received any requests from the load balancer. - MODE_OFF (WARNING): Autoscaling is turned off. The number of instances in the group won't change automatically. The autoscaling configuration is preserved. - MODE_ONLY_UP (WARNING): Autoscaling is in the "Autoscale only out" mode. The autoscaler can add instances but not remove any. - MORE_THAN_ONE_BACKEND_SERVICE (ERROR): The instance group cannot be autoscaled because it has more than one backend service attached to it. - NOT_ENOUGH_QUOTA_AVAILABLE (ERROR): There is insufficient quota for the necessary resources, such as CPU or number of instances. - REGION_RESOURCE_STOCKOUT (ERROR): Shown only for regional autoscalers: there is a resource stockout in the chosen region. - SCALING_TARGET_DOES_NOT_EXIST (ERROR): The target to be scaled does not exist. - UNSUPPORTED_MAX_RATE_LOAD_BALANCING_CONFIGURATION (ERROR): Autoscaling does not work with an HTTP/S load balancer that has been configured for maxRate. - ZONE_RESOURCE_STOCKOUT (ERROR): For zonal autoscalers: there is a resource stockout in the chosen zone. For regional autoscalers: in at least one of the zones you're using there is a resource stockout. New values might be added in the future. Some of the values might not be available in all API versions.

message str

The status message.

type str

The type of error, warning, or notice returned. Current set of possible values: - ALL_INSTANCES_UNHEALTHY (WARNING): All instances in the instance group are unhealthy (not in RUNNING state). - BACKEND_SERVICE_DOES_NOT_EXIST (ERROR): There is no backend service attached to the instance group. - CAPPED_AT_MAX_NUM_REPLICAS (WARNING): Autoscaler recommends a size greater than maxNumReplicas. - CUSTOM_METRIC_DATA_POINTS_TOO_SPARSE (WARNING): The custom metric samples are not exported often enough to be a credible base for autoscaling. - CUSTOM_METRIC_INVALID (ERROR): The custom metric that was specified does not exist or does not have the necessary labels. - MIN_EQUALS_MAX (WARNING): The minNumReplicas is equal to maxNumReplicas. This means the autoscaler cannot add or remove instances from the instance group. - MISSING_CUSTOM_METRIC_DATA_POINTS (WARNING): The autoscaler did not receive any data from the custom metric configured for autoscaling. - MISSING_LOAD_BALANCING_DATA_POINTS (WARNING): The autoscaler is configured to scale based on a load balancing signal but the instance group has not received any requests from the load balancer. - MODE_OFF (WARNING): Autoscaling is turned off. The number of instances in the group won't change automatically. The autoscaling configuration is preserved. - MODE_ONLY_UP (WARNING): Autoscaling is in the "Autoscale only out" mode. The autoscaler can add instances but not remove any. - MORE_THAN_ONE_BACKEND_SERVICE (ERROR): The instance group cannot be autoscaled because it has more than one backend service attached to it. - NOT_ENOUGH_QUOTA_AVAILABLE (ERROR): There is insufficient quota for the necessary resources, such as CPU or number of instances. - REGION_RESOURCE_STOCKOUT (ERROR): Shown only for regional autoscalers: there is a resource stockout in the chosen region. - SCALING_TARGET_DOES_NOT_EXIST (ERROR): The target to be scaled does not exist. - UNSUPPORTED_MAX_RATE_LOAD_BALANCING_CONFIGURATION (ERROR): Autoscaling does not work with an HTTP/S load balancer that has been configured for maxRate. - ZONE_RESOURCE_STOCKOUT (ERROR): For zonal autoscalers: there is a resource stockout in the chosen zone. For regional autoscalers: in at least one of the zones you're using there is a resource stockout. New values might be added in the future. Some of the values might not be available in all API versions.

message String

The status message.

type String

The type of error, warning, or notice returned. Current set of possible values: - ALL_INSTANCES_UNHEALTHY (WARNING): All instances in the instance group are unhealthy (not in RUNNING state). - BACKEND_SERVICE_DOES_NOT_EXIST (ERROR): There is no backend service attached to the instance group. - CAPPED_AT_MAX_NUM_REPLICAS (WARNING): Autoscaler recommends a size greater than maxNumReplicas. - CUSTOM_METRIC_DATA_POINTS_TOO_SPARSE (WARNING): The custom metric samples are not exported often enough to be a credible base for autoscaling. - CUSTOM_METRIC_INVALID (ERROR): The custom metric that was specified does not exist or does not have the necessary labels. - MIN_EQUALS_MAX (WARNING): The minNumReplicas is equal to maxNumReplicas. This means the autoscaler cannot add or remove instances from the instance group. - MISSING_CUSTOM_METRIC_DATA_POINTS (WARNING): The autoscaler did not receive any data from the custom metric configured for autoscaling. - MISSING_LOAD_BALANCING_DATA_POINTS (WARNING): The autoscaler is configured to scale based on a load balancing signal but the instance group has not received any requests from the load balancer. - MODE_OFF (WARNING): Autoscaling is turned off. The number of instances in the group won't change automatically. The autoscaling configuration is preserved. - MODE_ONLY_UP (WARNING): Autoscaling is in the "Autoscale only out" mode. The autoscaler can add instances but not remove any. - MORE_THAN_ONE_BACKEND_SERVICE (ERROR): The instance group cannot be autoscaled because it has more than one backend service attached to it. - NOT_ENOUGH_QUOTA_AVAILABLE (ERROR): There is insufficient quota for the necessary resources, such as CPU or number of instances. - REGION_RESOURCE_STOCKOUT (ERROR): Shown only for regional autoscalers: there is a resource stockout in the chosen region. - SCALING_TARGET_DOES_NOT_EXIST (ERROR): The target to be scaled does not exist. - UNSUPPORTED_MAX_RATE_LOAD_BALANCING_CONFIGURATION (ERROR): Autoscaling does not work with an HTTP/S load balancer that has been configured for maxRate. - ZONE_RESOURCE_STOCKOUT (ERROR): For zonal autoscalers: there is a resource stockout in the chosen zone. For regional autoscalers: in at least one of the zones you're using there is a resource stockout. New values might be added in the future. Some of the values might not be available in all API versions.

AutoscalingPolicy

CoolDownPeriodSec int

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

CpuUtilization Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyCpuUtilization

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

CustomMetricUtilizations List<Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyCustomMetricUtilization>

Configuration parameters of autoscaling based on a custom metric.

LoadBalancingUtilization Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyLoadBalancingUtilization

Configuration parameters of autoscaling based on load balancer.

MaxNumReplicas int

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

MinNumReplicas int

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

Mode Pulumi.GoogleNative.Compute.Alpha.AutoscalingPolicyMode

Defines operating mode for this policy.

ScaleDownControl Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyScaleDownControl
ScaleInControl Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyScaleInControl
ScalingSchedules Dictionary<string, string>

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

CoolDownPeriodSec int

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

CpuUtilization AutoscalingPolicyCpuUtilization

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

CustomMetricUtilizations []AutoscalingPolicyCustomMetricUtilization

Configuration parameters of autoscaling based on a custom metric.

LoadBalancingUtilization AutoscalingPolicyLoadBalancingUtilization

Configuration parameters of autoscaling based on load balancer.

MaxNumReplicas int

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

MinNumReplicas int

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

Mode AutoscalingPolicyMode

Defines operating mode for this policy.

ScaleDownControl AutoscalingPolicyScaleDownControl
ScaleInControl AutoscalingPolicyScaleInControl
ScalingSchedules map[string]string

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

coolDownPeriodSec Integer

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpuUtilization AutoscalingPolicyCpuUtilization

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

customMetricUtilizations ListPolicyCustomMetricUtilization>

Configuration parameters of autoscaling based on a custom metric.

loadBalancingUtilization AutoscalingPolicyLoadBalancingUtilization

Configuration parameters of autoscaling based on load balancer.

maxNumReplicas Integer

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

minNumReplicas Integer

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode AutoscalingPolicyMode

Defines operating mode for this policy.

scaleDownControl AutoscalingPolicyScaleDownControl
scaleInControl AutoscalingPolicyScaleInControl
scalingSchedules Map

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

coolDownPeriodSec number

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpuUtilization AutoscalingPolicyCpuUtilization

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

customMetricUtilizations AutoscalingPolicyCustomMetricUtilization[]

Configuration parameters of autoscaling based on a custom metric.

loadBalancingUtilization AutoscalingPolicyLoadBalancingUtilization

Configuration parameters of autoscaling based on load balancer.

maxNumReplicas number

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

minNumReplicas number

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode AutoscalingPolicyMode

Defines operating mode for this policy.

scaleDownControl AutoscalingPolicyScaleDownControl
scaleInControl AutoscalingPolicyScaleInControl
scalingSchedules {[key: string]: string}

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

cool_down_period_sec int

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpu_utilization AutoscalingPolicyCpuUtilization

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

custom_metric_utilizations Sequence[AutoscalingPolicyCustomMetricUtilization]

Configuration parameters of autoscaling based on a custom metric.

load_balancing_utilization AutoscalingPolicyLoadBalancingUtilization

Configuration parameters of autoscaling based on load balancer.

max_num_replicas int

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

min_num_replicas int

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode AutoscalingPolicyMode

Defines operating mode for this policy.

scale_down_control AutoscalingPolicyScaleDownControl
scale_in_control AutoscalingPolicyScaleInControl
scaling_schedules Mapping[str, str]

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

coolDownPeriodSec Number

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpuUtilization Property Map

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

customMetricUtilizations List

Configuration parameters of autoscaling based on a custom metric.

loadBalancingUtilization Property Map

Configuration parameters of autoscaling based on load balancer.

maxNumReplicas Number

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

minNumReplicas Number

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode "OFF" | "ON" | "ONLY_SCALE_OUT" | "ONLY_UP"

Defines operating mode for this policy.

scaleDownControl Property Map
scaleInControl Property Map
scalingSchedules Map

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

AutoscalingPolicyCpuUtilization

PredictiveMethod Pulumi.GoogleNative.Compute.Alpha.AutoscalingPolicyCpuUtilizationPredictiveMethod

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

UtilizationTarget double

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

PredictiveMethod AutoscalingPolicyCpuUtilizationPredictiveMethod

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

UtilizationTarget float64

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictiveMethod AutoscalingPolicyCpuUtilizationPredictiveMethod

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilizationTarget Double

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictiveMethod AutoscalingPolicyCpuUtilizationPredictiveMethod

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilizationTarget number

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictive_method AutoscalingPolicyCpuUtilizationPredictiveMethod

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilization_target float

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictiveMethod "NONE" | "OPTIMIZE_AVAILABILITY" | "PREDICTIVE_METHOD_UNSPECIFIED" | "STANDARD"

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilizationTarget Number

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

AutoscalingPolicyCpuUtilizationPredictiveMethod

None
NONE

No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics

OptimizeAvailability
OPTIMIZE_AVAILABILITY

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

PredictiveMethodUnspecified
PREDICTIVE_METHOD_UNSPECIFIED
Standard
STANDARD

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand. This value is being DEPRECATED - it won't be promoted to beta and v1. Use OPTIMIZE_AVAILABILITY instead.

AutoscalingPolicyCpuUtilizationPredictiveMethodNone
NONE

No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics

AutoscalingPolicyCpuUtilizationPredictiveMethodOptimizeAvailability
OPTIMIZE_AVAILABILITY

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

AutoscalingPolicyCpuUtilizationPredictiveMethodPredictiveMethodUnspecified
PREDICTIVE_METHOD_UNSPECIFIED
AutoscalingPolicyCpuUtilizationPredictiveMethodStandard
STANDARD

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand. This value is being DEPRECATED - it won't be promoted to beta and v1. Use OPTIMIZE_AVAILABILITY instead.

None
NONE

No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics

OptimizeAvailability
OPTIMIZE_AVAILABILITY

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

PredictiveMethodUnspecified
PREDICTIVE_METHOD_UNSPECIFIED
Standard
STANDARD

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand. This value is being DEPRECATED - it won't be promoted to beta and v1. Use OPTIMIZE_AVAILABILITY instead.

None
NONE

No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics

OptimizeAvailability
OPTIMIZE_AVAILABILITY

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

PredictiveMethodUnspecified
PREDICTIVE_METHOD_UNSPECIFIED
Standard
STANDARD

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand. This value is being DEPRECATED - it won't be promoted to beta and v1. Use OPTIMIZE_AVAILABILITY instead.

NONE
NONE

No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics

OPTIMIZE_AVAILABILITY
OPTIMIZE_AVAILABILITY

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

PREDICTIVE_METHOD_UNSPECIFIED
PREDICTIVE_METHOD_UNSPECIFIED
STANDARD
STANDARD

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand. This value is being DEPRECATED - it won't be promoted to beta and v1. Use OPTIMIZE_AVAILABILITY instead.

"NONE"
NONE

No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics

"OPTIMIZE_AVAILABILITY"
OPTIMIZE_AVAILABILITY

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

"PREDICTIVE_METHOD_UNSPECIFIED"
PREDICTIVE_METHOD_UNSPECIFIED
"STANDARD"
STANDARD

Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand. This value is being DEPRECATED - it won't be promoted to beta and v1. Use OPTIMIZE_AVAILABILITY instead.

AutoscalingPolicyCpuUtilizationResponse

PredictiveMethod string

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

UtilizationTarget double

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

PredictiveMethod string

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

UtilizationTarget float64

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictiveMethod String

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilizationTarget Double

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictiveMethod string

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilizationTarget number

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictive_method str

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilization_target float

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

predictiveMethod String

Indicates whether predictive autoscaling based on CPU metric is enabled. Valid values are: * NONE (default). No predictive method is used. The autoscaler scales the group to meet current demand based on real-time metrics. * OPTIMIZE_AVAILABILITY. Predictive autoscaling improves availability by monitoring daily and weekly load patterns and scaling out ahead of anticipated demand.

utilizationTarget Number

The target CPU utilization that the autoscaler maintains. Must be a float value in the range (0, 1]. If not specified, the default is 0.6. If the CPU level is below the target utilization, the autoscaler scales in the number of instances until it reaches the minimum number of instances you specified or until the average CPU of your instances reaches the target utilization. If the average CPU is above the target utilization, the autoscaler scales out until it reaches the maximum number of instances you specified or until the average utilization reaches the target utilization.

AutoscalingPolicyCustomMetricUtilization

Filter string

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

Metric string

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

SingleInstanceAssignment double

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

UtilizationTarget double

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

UtilizationTargetType Pulumi.GoogleNative.Compute.Alpha.AutoscalingPolicyCustomMetricUtilizationUtilizationTargetType

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

Filter string

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

Metric string

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

SingleInstanceAssignment float64

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

UtilizationTarget float64

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

UtilizationTargetType AutoscalingPolicyCustomMetricUtilizationUtilizationTargetType

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter String

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric String

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

singleInstanceAssignment Double

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilizationTarget Double

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilizationTargetType AutoscalingPolicyCustomMetricUtilizationUtilizationTargetType

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter string

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric string

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

singleInstanceAssignment number

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilizationTarget number

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilizationTargetType AutoscalingPolicyCustomMetricUtilizationUtilizationTargetType

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter str

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric str

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

single_instance_assignment float

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilization_target float

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilization_target_type AutoscalingPolicyCustomMetricUtilizationUtilizationTargetType

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter String

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric String

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

singleInstanceAssignment Number

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilizationTarget Number

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilizationTargetType "DELTA_PER_MINUTE" | "DELTA_PER_SECOND" | "GAUGE"

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

AutoscalingPolicyCustomMetricUtilizationResponse

Filter string

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

Metric string

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

SingleInstanceAssignment double

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

UtilizationTarget double

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

UtilizationTargetType string

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

Filter string

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

Metric string

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

SingleInstanceAssignment float64

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

UtilizationTarget float64

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

UtilizationTargetType string

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter String

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric String

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

singleInstanceAssignment Double

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilizationTarget Double

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilizationTargetType String

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter string

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric string

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

singleInstanceAssignment number

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilizationTarget number

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilizationTargetType string

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter str

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric str

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

single_instance_assignment float

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilization_target float

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilization_target_type str

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

filter String

A filter string, compatible with a Stackdriver Monitoring filter string for TimeSeries.list API call. This filter is used to select a specific TimeSeries for the purpose of autoscaling and to determine whether the metric is exporting per-instance or per-group data. For the filter to be valid for autoscaling purposes, the following rules apply: - You can only use the AND operator for joining selectors. - You can only use direct equality comparison operator (=) without any functions for each selector. - You can specify the metric in both the filter string and in the metric field. However, if specified in both places, the metric must be identical. - The monitored resource type determines what kind of values are expected for the metric. If it is a gce_instance, the autoscaler expects the metric to include a separate TimeSeries for each instance in a group. In such a case, you cannot filter on resource labels. If the resource type is any other value, the autoscaler expects this metric to contain values that apply to the entire autoscaled instance group and resource label filtering can be performed to point autoscaler at the correct TimeSeries to scale upon. This is called a per-group metric for the purpose of autoscaling. If not specified, the type defaults to gce_instance. Try to provide a filter that is selective enough to pick just one TimeSeries for the autoscaled group or for each of the instances (if you are using gce_instance resource type). If multiple TimeSeries are returned upon the query execution, the autoscaler will sum their respective values to obtain its scaling value.

metric String

The identifier (type) of the Stackdriver Monitoring metric. The metric cannot have negative values. The metric must have a value type of INT64 or DOUBLE.

singleInstanceAssignment Number

If scaling is based on a per-group metric value that represents the total amount of work to be done or resource usage, set this value to an amount assigned for a single instance of the scaled group. Autoscaler keeps the number of instances proportional to the value of this metric. The metric itself does not change value due to group resizing. A good metric to use with the target is for example pubsub.googleapis.com/subscription/num_undelivered_messages or a custom metric exporting the total number of requests coming to your instances. A bad example would be a metric exporting an average or median latency, since this value can't include a chunk assignable to a single instance, it could be better used with utilization_target instead.

utilizationTarget Number

The target value of the metric that autoscaler maintains. This must be a positive value. A utilization metric scales number of virtual machines handling requests to increase or decrease proportionally to the metric. For example, a good metric to use as a utilization_target is https://www.googleapis.com/compute/v1/instance/network/received_bytes_count. The autoscaler works to keep this value constant for each of the instances.

utilizationTargetType String

Defines how target utilization value is expressed for a Stackdriver Monitoring metric. Either GAUGE, DELTA_PER_SECOND, or DELTA_PER_MINUTE.

AutoscalingPolicyCustomMetricUtilizationUtilizationTargetType

DeltaPerMinute
DELTA_PER_MINUTE

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per minute.

DeltaPerSecond
DELTA_PER_SECOND

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per second.

Gauge
GAUGE

Sets the utilization target value for a gauge metric. The autoscaler will collect the average utilization of the virtual machines from the last couple of minutes, and compare the value to the utilization target value to perform autoscaling.

AutoscalingPolicyCustomMetricUtilizationUtilizationTargetTypeDeltaPerMinute
DELTA_PER_MINUTE

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per minute.

AutoscalingPolicyCustomMetricUtilizationUtilizationTargetTypeDeltaPerSecond
DELTA_PER_SECOND

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per second.

AutoscalingPolicyCustomMetricUtilizationUtilizationTargetTypeGauge
GAUGE

Sets the utilization target value for a gauge metric. The autoscaler will collect the average utilization of the virtual machines from the last couple of minutes, and compare the value to the utilization target value to perform autoscaling.

DeltaPerMinute
DELTA_PER_MINUTE

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per minute.

DeltaPerSecond
DELTA_PER_SECOND

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per second.

Gauge
GAUGE

Sets the utilization target value for a gauge metric. The autoscaler will collect the average utilization of the virtual machines from the last couple of minutes, and compare the value to the utilization target value to perform autoscaling.

DeltaPerMinute
DELTA_PER_MINUTE

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per minute.

DeltaPerSecond
DELTA_PER_SECOND

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per second.

Gauge
GAUGE

Sets the utilization target value for a gauge metric. The autoscaler will collect the average utilization of the virtual machines from the last couple of minutes, and compare the value to the utilization target value to perform autoscaling.

DELTA_PER_MINUTE
DELTA_PER_MINUTE

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per minute.

DELTA_PER_SECOND
DELTA_PER_SECOND

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per second.

GAUGE
GAUGE

Sets the utilization target value for a gauge metric. The autoscaler will collect the average utilization of the virtual machines from the last couple of minutes, and compare the value to the utilization target value to perform autoscaling.

"DELTA_PER_MINUTE"
DELTA_PER_MINUTE

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per minute.

"DELTA_PER_SECOND"
DELTA_PER_SECOND

Sets the utilization target value for a cumulative or delta metric, expressed as the rate of growth per second.

"GAUGE"
GAUGE

Sets the utilization target value for a gauge metric. The autoscaler will collect the average utilization of the virtual machines from the last couple of minutes, and compare the value to the utilization target value to perform autoscaling.

AutoscalingPolicyLoadBalancingUtilization

UtilizationTarget double

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

UtilizationTarget float64

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilizationTarget Double

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilizationTarget number

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilization_target float

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilizationTarget Number

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

AutoscalingPolicyLoadBalancingUtilizationResponse

UtilizationTarget double

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

UtilizationTarget float64

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilizationTarget Double

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilizationTarget number

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilization_target float

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

utilizationTarget Number

Fraction of backend capacity utilization (set in HTTP(S) load balancing configuration) that the autoscaler maintains. Must be a positive float value. If not defined, the default is 0.8.

AutoscalingPolicyMode

Off
OFF

Do not automatically scale the MIG in or out. The recommended_size field contains the size of MIG that would be set if the actuation mode was enabled.

On
ON

Automatically scale the MIG in and out according to the policy.

OnlyScaleOut
ONLY_SCALE_OUT

Automatically create VMs according to the policy, but do not scale the MIG in.

OnlyUp
ONLY_UP

Automatically create VMs according to the policy, but do not scale the MIG in.

AutoscalingPolicyModeOff
OFF

Do not automatically scale the MIG in or out. The recommended_size field contains the size of MIG that would be set if the actuation mode was enabled.

AutoscalingPolicyModeOn
ON

Automatically scale the MIG in and out according to the policy.

AutoscalingPolicyModeOnlyScaleOut
ONLY_SCALE_OUT

Automatically create VMs according to the policy, but do not scale the MIG in.

AutoscalingPolicyModeOnlyUp
ONLY_UP

Automatically create VMs according to the policy, but do not scale the MIG in.

Off
OFF

Do not automatically scale the MIG in or out. The recommended_size field contains the size of MIG that would be set if the actuation mode was enabled.

On
ON

Automatically scale the MIG in and out according to the policy.

OnlyScaleOut
ONLY_SCALE_OUT

Automatically create VMs according to the policy, but do not scale the MIG in.

OnlyUp
ONLY_UP

Automatically create VMs according to the policy, but do not scale the MIG in.

Off
OFF

Do not automatically scale the MIG in or out. The recommended_size field contains the size of MIG that would be set if the actuation mode was enabled.

On
ON

Automatically scale the MIG in and out according to the policy.

OnlyScaleOut
ONLY_SCALE_OUT

Automatically create VMs according to the policy, but do not scale the MIG in.

OnlyUp
ONLY_UP

Automatically create VMs according to the policy, but do not scale the MIG in.

OFF
OFF

Do not automatically scale the MIG in or out. The recommended_size field contains the size of MIG that would be set if the actuation mode was enabled.

ON
ON

Automatically scale the MIG in and out according to the policy.

ONLY_SCALE_OUT
ONLY_SCALE_OUT

Automatically create VMs according to the policy, but do not scale the MIG in.

ONLY_UP
ONLY_UP

Automatically create VMs according to the policy, but do not scale the MIG in.

"OFF"
OFF

Do not automatically scale the MIG in or out. The recommended_size field contains the size of MIG that would be set if the actuation mode was enabled.

"ON"
ON

Automatically scale the MIG in and out according to the policy.

"ONLY_SCALE_OUT"
ONLY_SCALE_OUT

Automatically create VMs according to the policy, but do not scale the MIG in.

"ONLY_UP"
ONLY_UP

Automatically create VMs according to the policy, but do not scale the MIG in.

AutoscalingPolicyResponse

CoolDownPeriodSec int

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

CpuUtilization Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyCpuUtilizationResponse

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

CustomMetricUtilizations List<Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyCustomMetricUtilizationResponse>

Configuration parameters of autoscaling based on a custom metric.

LoadBalancingUtilization Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyLoadBalancingUtilizationResponse

Configuration parameters of autoscaling based on load balancer.

MaxNumReplicas int

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

MinNumReplicas int

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

Mode string

Defines operating mode for this policy.

ScaleDownControl Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyScaleDownControlResponse
ScaleInControl Pulumi.GoogleNative.Compute.Alpha.Inputs.AutoscalingPolicyScaleInControlResponse
ScalingSchedules Dictionary<string, string>

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

CoolDownPeriodSec int

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

CpuUtilization AutoscalingPolicyCpuUtilizationResponse

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

CustomMetricUtilizations []AutoscalingPolicyCustomMetricUtilizationResponse

Configuration parameters of autoscaling based on a custom metric.

LoadBalancingUtilization AutoscalingPolicyLoadBalancingUtilizationResponse

Configuration parameters of autoscaling based on load balancer.

MaxNumReplicas int

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

MinNumReplicas int

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

Mode string

Defines operating mode for this policy.

ScaleDownControl AutoscalingPolicyScaleDownControlResponse
ScaleInControl AutoscalingPolicyScaleInControlResponse
ScalingSchedules map[string]string

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

coolDownPeriodSec Integer

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpuUtilization AutoscalingPolicyCpuUtilizationResponse

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

customMetricUtilizations ListPolicyCustomMetricUtilizationResponse>

Configuration parameters of autoscaling based on a custom metric.

loadBalancingUtilization AutoscalingPolicyLoadBalancingUtilizationResponse

Configuration parameters of autoscaling based on load balancer.

maxNumReplicas Integer

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

minNumReplicas Integer

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode String

Defines operating mode for this policy.

scaleDownControl AutoscalingPolicyScaleDownControlResponse
scaleInControl AutoscalingPolicyScaleInControlResponse
scalingSchedules Map

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

coolDownPeriodSec number

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpuUtilization AutoscalingPolicyCpuUtilizationResponse

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

customMetricUtilizations AutoscalingPolicyCustomMetricUtilizationResponse[]

Configuration parameters of autoscaling based on a custom metric.

loadBalancingUtilization AutoscalingPolicyLoadBalancingUtilizationResponse

Configuration parameters of autoscaling based on load balancer.

maxNumReplicas number

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

minNumReplicas number

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode string

Defines operating mode for this policy.

scaleDownControl AutoscalingPolicyScaleDownControlResponse
scaleInControl AutoscalingPolicyScaleInControlResponse
scalingSchedules {[key: string]: string}

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

cool_down_period_sec int

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpu_utilization AutoscalingPolicyCpuUtilizationResponse

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

custom_metric_utilizations Sequence[AutoscalingPolicyCustomMetricUtilizationResponse]

Configuration parameters of autoscaling based on a custom metric.

load_balancing_utilization AutoscalingPolicyLoadBalancingUtilizationResponse

Configuration parameters of autoscaling based on load balancer.

max_num_replicas int

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

min_num_replicas int

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode str

Defines operating mode for this policy.

scale_down_control AutoscalingPolicyScaleDownControlResponse
scale_in_control AutoscalingPolicyScaleInControlResponse
scaling_schedules Mapping[str, str]

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

coolDownPeriodSec Number

The number of seconds that the autoscaler waits before it starts collecting information from a new instance. This prevents the autoscaler from collecting information when the instance is initializing, during which the collected usage would not be reliable. The default time autoscaler waits is 60 seconds. Virtual machine initialization times might vary because of numerous factors. We recommend that you test how long an instance may take to initialize. To do this, create an instance and time the startup process.

cpuUtilization Property Map

Defines the CPU utilization policy that allows the autoscaler to scale based on the average CPU utilization of a managed instance group.

customMetricUtilizations List

Configuration parameters of autoscaling based on a custom metric.

loadBalancingUtilization Property Map

Configuration parameters of autoscaling based on load balancer.

maxNumReplicas Number

The maximum number of instances that the autoscaler can scale out to. This is required when creating or updating an autoscaler. The maximum number of replicas must not be lower than minimal number of replicas.

minNumReplicas Number

The minimum number of replicas that the autoscaler can scale in to. This cannot be less than 0. If not provided, autoscaler chooses a default value depending on maximum number of instances allowed.

mode String

Defines operating mode for this policy.

scaleDownControl Property Map
scaleInControl Property Map
scalingSchedules Map

Scaling schedules defined for an autoscaler. Multiple schedules can be set on an autoscaler, and they can overlap. During overlapping periods the greatest min_required_replicas of all scaling schedules is applied. Up to 128 scaling schedules are allowed.

AutoscalingPolicyScaleDownControl

MaxScaledDownReplicas Pulumi.GoogleNative.Compute.Alpha.Inputs.FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

MaxScaledDownReplicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledDownReplicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Integer

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledDownReplicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

max_scaled_down_replicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

time_window_sec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledDownReplicas Property Map

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

AutoscalingPolicyScaleDownControlResponse

MaxScaledDownReplicas Pulumi.GoogleNative.Compute.Alpha.Inputs.FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

MaxScaledDownReplicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledDownReplicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Integer

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledDownReplicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

max_scaled_down_replicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

time_window_sec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledDownReplicas Property Map

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

AutoscalingPolicyScaleInControl

MaxScaledInReplicas Pulumi.GoogleNative.Compute.Alpha.Inputs.FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

MaxScaledInReplicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledInReplicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Integer

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledInReplicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

max_scaled_in_replicas FixedOrPercent

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

time_window_sec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledInReplicas Property Map

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

AutoscalingPolicyScaleInControlResponse

MaxScaledInReplicas Pulumi.GoogleNative.Compute.Alpha.Inputs.FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

MaxScaledInReplicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

TimeWindowSec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledInReplicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Integer

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledInReplicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

max_scaled_in_replicas FixedOrPercentResponse

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

time_window_sec int

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

maxScaledInReplicas Property Map

Maximum allowed number (or %) of VMs that can be deducted from the peak recommendation during the window autoscaler looks at when computing recommendations. Possibly all these VMs can be deleted at once so user service needs to be prepared to lose that many VMs in one step.

timeWindowSec Number

How far back autoscaling looks when computing recommendations to include directives regarding slower scale in, as described above.

FixedOrPercent

Fixed int

Specifies a fixed number of VM instances. This must be a positive integer.

Percent int

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

Fixed int

Specifies a fixed number of VM instances. This must be a positive integer.

Percent int

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

fixed Integer

Specifies a fixed number of VM instances. This must be a positive integer.

percent Integer

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

fixed number

Specifies a fixed number of VM instances. This must be a positive integer.

percent number

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

fixed int

Specifies a fixed number of VM instances. This must be a positive integer.

percent int

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

fixed Number

Specifies a fixed number of VM instances. This must be a positive integer.

percent Number

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

FixedOrPercentResponse

Calculated int

Absolute value of VM instances calculated based on the specific mode. - If the value is fixed, then the calculated value is equal to the fixed value. - If the value is a percent, then the calculated value is percent/100 * targetSize. For example, the calculated value of a 80% of a managed instance group with 150 instances would be (80/100 * 150) = 120 VM instances. If there is a remainder, the number is rounded.

Fixed int

Specifies a fixed number of VM instances. This must be a positive integer.

Percent int

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

Calculated int

Absolute value of VM instances calculated based on the specific mode. - If the value is fixed, then the calculated value is equal to the fixed value. - If the value is a percent, then the calculated value is percent/100 * targetSize. For example, the calculated value of a 80% of a managed instance group with 150 instances would be (80/100 * 150) = 120 VM instances. If there is a remainder, the number is rounded.

Fixed int

Specifies a fixed number of VM instances. This must be a positive integer.

Percent int

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

calculated Integer

Absolute value of VM instances calculated based on the specific mode. - If the value is fixed, then the calculated value is equal to the fixed value. - If the value is a percent, then the calculated value is percent/100 * targetSize. For example, the calculated value of a 80% of a managed instance group with 150 instances would be (80/100 * 150) = 120 VM instances. If there is a remainder, the number is rounded.

fixed Integer

Specifies a fixed number of VM instances. This must be a positive integer.

percent Integer

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

calculated number

Absolute value of VM instances calculated based on the specific mode. - If the value is fixed, then the calculated value is equal to the fixed value. - If the value is a percent, then the calculated value is percent/100 * targetSize. For example, the calculated value of a 80% of a managed instance group with 150 instances would be (80/100 * 150) = 120 VM instances. If there is a remainder, the number is rounded.

fixed number

Specifies a fixed number of VM instances. This must be a positive integer.

percent number

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

calculated int

Absolute value of VM instances calculated based on the specific mode. - If the value is fixed, then the calculated value is equal to the fixed value. - If the value is a percent, then the calculated value is percent/100 * targetSize. For example, the calculated value of a 80% of a managed instance group with 150 instances would be (80/100 * 150) = 120 VM instances. If there is a remainder, the number is rounded.

fixed int

Specifies a fixed number of VM instances. This must be a positive integer.

percent int

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

calculated Number

Absolute value of VM instances calculated based on the specific mode. - If the value is fixed, then the calculated value is equal to the fixed value. - If the value is a percent, then the calculated value is percent/100 * targetSize. For example, the calculated value of a 80% of a managed instance group with 150 instances would be (80/100 * 150) = 120 VM instances. If there is a remainder, the number is rounded.

fixed Number

Specifies a fixed number of VM instances. This must be a positive integer.

percent Number

Specifies a percentage of instances between 0 to 100%, inclusive. For example, specify 80 for 80%.

Package Details

Repository
https://github.com/pulumi/pulumi-google-native
License
Apache-2.0