1. Automated Root Cause Analysis in AI Systems with Dynatrace


    In order to implement automated root cause analysis in AI systems with Dynatrace, you would typically integrate Dynatrace's comprehensive monitoring capabilities within your system. Dynatrace is a cloud-based software intelligence platform that provides full-stack monitoring, from the front-end to the back-end, infrastructure, and networks. With Pulumi, you can automate the provisioning and setup of Dynatrace resources necessary for monitoring your AI system.

    Below, I will provide you with a Pulumi program in Python that sets up Dynatrace resources, including setting up policies for alerting and monitoring technologies. This will allow Dynatrace to monitor the AI system and perform automated root cause analysis when issues arise.

    The resources we'll create include:

    • PolicyBindings: Used to apply monitoring policies within specified groups or environments.
    • MonitoredTechnologiesIis: Enables or disables monitoring for specific technologies such as IIS (Internet Information Services).
    • ServiceAnomalies: Sets up anomaly detection rules for services to help with identifying problems.
    • Alerting: Defines the alerting rules, which will notify you in case of detected issues.

    Here's the Pulumi program:

    import pulumi import pulumi_dynatrace as dynatrace # Policy bindings that apply monitoring policies to specified environments or groups. policy_bindings = dynatrace.PolicyBindings("policy-bindings", group="GROUP_IDENTIFIER", policies=[ "POLICY_ID_1", "POLICY_ID_2", # Add more policy IDs as necessary. ], # Replace 'ENVIRONMENT' with the target environment identifier. environment="ENVIRONMENT" ) # Monitored Technologies setup. This example uses IIS. # Replace 'HOST_ID' with the ID of the host you want to monitor. monitored_tech_iis = dynatrace.MonitoredTechnologiesIis("monitored-tech-iis", hostId="HOST_ID", enabled=True # Set this to False to disable monitoring. ) # Service anomalies configuration to define the rules for anomaly detection. service_anomalies = dynatrace.ServiceAnomalies("service-anomalies", load={ "drops": { "minutes": 5, # Time frame for drop/spike detection. "percent": 10 # Percentage threshold for drops. }, "spikes": { "minutes": 5, # Time frame for drop/spike detection. "percent": 20 # Percentage threshold for spikes. } }, responseTimes={ "auto": { "percent": 75, # Detection for slowest percentage of transactions. "milliseconds": 1000 # Threshold for response time in milliseconds. } } ) # Alerting setup to define what triggers an alert. alerting_policy = dynatrace.Alerting("alerting-policy", name="High CPU Usage Alert", # Name this policy logically based on the alert it represents. rules={ "includeMode": "INCLUDE_ANY", "severityLevel": "SEVERITY_LEVEL", "delayInMinutes": 0 # Immediate notification without delay. }, filters={ "custom": { "title": { "value": "High CPU usage detected", # Alert title. "operator": "CONTAINS" }, "description": { "value": "The CPU usage has exceeded the threshold", # Detailed description of the alert. "operator": "CONTAINS" } } }, legacyId="LEGACY_ALERT_ID" # You might need this for existing alerts. ) # After defining your resources, you can export their IDs or other important attributes: pulumi.export('policy_bindings_id', policy_bindings.id) pulumi.export('monitored_tech_iis_id', monitored_tech_iis.id) pulumi.export('service_anomalies_id', service_anomalies.id) pulumi.export('alerting_policy_id', alerting_policy.id)

    Make sure to replace placeholders like GROUP_IDENTIFIER, POLICY_ID_1, ENVIRONMENT, HOST_ID, SEVERITY_LEVEL, and LEGACY_ALERT_ID with actual values corresponding to your Dynatrace and environment setup.

    This Pulumi program sets up the necessary configuration in Dynatrace for monitoring and alerting, which are crucial components of automated root cause analysis in an AI system. When you run this Pulumi program, it provisions the resources in your Dynatrace account, assuming you have the correct access rights and API tokens configured within your Pulumi environment to interact with Dynatrace API.

    By accurately configuring AI system monitoring and alerting, Dynatrace can provide automated root cause analyses when performance anomalies are detected, allowing for rapid and informed responses to incidents.