Autoscaling AI Workloads with Alicloud SLB

Question

Pulumi · Accepted Answer

Autoscaling within a cloud environment is a method to automatically adjust the number of compute resources within your cloud environment. This means that as your workload demand increases or decreases, your environment will scale up or down by adding or removing resources to meet the current demand, which is especially useful for workloads with fluctuating needs, such as AI applications.

In this context, Alicloud (Alibaba Cloud) provides a reliable infrastructure for implementing autoscaling in combination with the Server Load Balancer (SLB) service. SLB distributes incoming traffic among multiple instances to enhance the service capability of your applications. The Elastic Compute Service (ECS) with SLB can be configured with autoscaling to ensure that your AI workload can automatically adjust to incoming traffic demands.

To create such an environment, you would generally use the following Alicloud services:

1. **Elastic Compute Service (ECS)**: This is where your AI workloads will actually run. You can define ECS instances with specific CPU, memory, and storage configurations that are suitable for your AI tasks.

2. **Server Load Balancer (SLB)**: The SLB will distribute incoming traffic across the ECS instances to balance the load. This ensures that no single instance is overwhelmed, which can help maintain the responsiveness of your AI applications.

3. **Auto Scaling Service**: This service automatically adjusts the number of ECS instances according to your configuration. When workload demand goes up, the service can automatically provision new instances and integrate them with the SLB. When demand goes down, it can remove instances to help you save costs.

4. **Essential Resource Elements**: Other resources like VPCs, security groups, and disk volumes which are the foundational components for networking, security, and data storage of your ECS instances.

Below is a Pulumi program in Python that sets up an autoscaling AI workload with Alicloud SLB. For the purpose of this example, the program will not include the AI-specific configuration of the ECS (like GPU types and specialized storage), as these details depend on your particular AI workload. However, the program will show you how to set up the autoscaling group and the SLB. Please further customize the ECS configuration to match your specific AI requirements.

```python
import pulumi
import pulumi_alicloud as alicloud

# Below are the basic configurations for your ECS instances, SLB, and autoscaling settings.
# You need to replace these with your actual application specifications.

# Create a new VPC for your resources to ensure your networking is separate.
vpc = alicloud.vpc.Network("ai-vpc", cidr_block="10.0.0.0/16")

# Create a new subnet inside your VPC.
subnet = alicloud.vpc.VSwitch("ai-vswitch", vpc_id=vpc.id, cidr_block="10.0.1.0/24", zone_id="cn-hangzhou-g")

# Create a new security group to allow inbound traffic.
security_group = alicloud.ecs.SecurityGroup("ai-sg", vpc_id=vpc.id)

# Create a new SLB instance.
# You can configure listeners and rules according to your specific application needs.
slb = alicloud.slb.LoadBalancer("ai-slb", vpc_id=vpc.id, address_type="internet")

# Set up an auto-scaling group that defines the minimum and maximum number of instances.
scaling_group = alicloud.ess.ScalingGroup("ai-scaling-group",
    vswitch_ids=[subnet.id],
    min_size=1,
    max_size=3,
    removal_policies=["OldestInstance", "NewestInstance"],
    health_check_type="ECS"
)

# Define an auto-scaling configuration for your ECS instances.
# This configuration includes the setup necessary for your AI workload, such as instance type and image ID.
scaling_configuration = alicloud.ess.ScalingConfiguration("ai-scaling-configuration",
    scaling_group_id=scaling_group.id,
    image_id="aliyun_image_id_of_your_choice",
    instance_types=["ecs.gn6v-c10g1.20xlarge"],  # Example instance type suitable for AI workloads
    security_group_id=security_group.id,
    enable_public_ip=True # If your workload requires access to the internet
    # Other configurations such as disk setups and instance details can be specified here.
)

# Define auto-scaling rules that control when to add or remove instances.
scale_up_rule = alicloud.ess.ScalingRule("ai-scale-up-rule",
    scaling_group_id=scaling_group.id,
    adjustment_type="QuantityChangeInCapacity",
    adjustment_value=1,
    cooldown=300
)

scale_down_rule = alicloud.ess.ScalingRule("ai-scale-down-rule",
    scaling_group_id=scaling_group.id,
    adjustment_type="QuantityChangeInCapacity",
    adjustment_value=-1,
    cooldown=300
)

# Create a listener for the SLB to receive traffic.
listener = alicloud.slb.Listener("ai-listener",
    load_balancer_id=slb.id,
    frontend_port=80,  # Your service port
    protocol="http",
    bandwidth=-1,
)

# Attach the scaling group to the SLB so newly launched instances are automatically added to the load balancer.
slb_attachment = alicloud.ess.SlbAttachment("ai-slb-attachment",
    scaling_group_id=scaling_group.id,
    load_balancer_ids=[slb.id]
)

# The following exports can be used to retrieve the VPC, subnet, and SLB information.
# Useful for debugging or when passing this stack's output to other stacks.

pulumi.export('vpc_id', vpc.id)
pulumi.export('subnet_id', subnet.id)
pulumi.export('slb_id', slb.id)
```

This code sets up the necessary services and resources for an autoscaling environment in Alicloud. When deploying this stack, you'll need to adjust the configurations, such as instance types, scaling policies, VPC, and SLB settings, according to your specific requirements. Once the setup is in place, you should monitor and adjust the autoscaling rules and SLB listeners based on traffic patterns and application performance.