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Optimizing cost of a Kubernetes microservice while preserving SLOs in production

In this example, you will use Akamas live optimization to minimize the cost of a Kubernetes deployment, while preserving application performance and reliability requirements.

Prerequisites

In this example, you need:

an Akamas instance
a Kubernetes cluster, with a deployment to be optimized
the kubectl command installed in the Akamas instance, configured to access the target Kubernetes and with privileges to get and update the deployment configurations
a supported telemetry data source (e.g. Prometheus or Dynatrace) configured to collect metrics from the target Kubernetes cluster

Optimization setup

Optimization packs

This example leverages the following optimization packs:

System

The system represents the Kubernetes deployment to be optimized (let's call it "frontend"). You can create a system.yaml manifest like this:

name: frontend
description: Kubernetes frontend deployment

Create the new system resource:

akamas create system system.yaml

The system will then have two components:

A Kubernetes container component, which contains container-level metrics like CPU usage and parameters to be tuned like CPU limits
A Web Application component, which contains service-level metrics like throughput and response time

In this example, we assume the deployment to be optimized is called frontend, with a container named server, and is located within the boutique namespace. We also assume that Dynatrace is used as a telemetry provider.

Kubernetes component

Create a component-container.yaml manifest like the following:

name: container
description: Kubernetes container, part of the frontend deployment
componentType: Kubernetes Container
properties:
  dynatrace:
    type: CONTAINER_GROUP_INSTANCE
    kubernetes:
      namespace: boutique
      containerName: server
      basePodName: frontend-*

Then run:

akamas create component component-container.yaml frontend

Now create a component-webapp.yaml manifest like the following:

name: webapp
description: The service related to the frontend deployment
componentType: Web Application
properties:
  dynatrace:
    id: <TELEMETRY_DYNATRACE_WEBAPP_ID>

Then run:

akamas create component component-webapp.yaml frontend

Workflow

The workflow in this example is composed of three main steps:

Update the Kubernetes deployment manifest with the Akamas recommended deployment parameters (CPU and memory limits)
Apply the new parameters (kubectl apply)
Wait for the rollout to complete
Sleep for 30 minutes (observation interval)

Create a workflow.yaml manifest like the following:

name: frontend
tasks:
  - name: configure
    operator: FileConfigurator
    arguments:
      source:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
        path: frontend.yaml.templ
      target:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
        path: frontend.yaml

  - name: apply
    operator: Executor
    arguments:
      timeout: 5m
      host:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
      command: kubectl apply -f frontend.yaml

  - name: verify
    operator: Executor
    arguments:
      timeout: 5m
      host:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
      command: kubectl rollout status --timeout=5m deployment/frontend -n boutique;

  - name: observe
    operator: Sleep
    arguments:
      seconds: 1800

Then run:

akamas create workflow workflow.yaml

Telemetry

Create the telemetry.yamlmanifest like the following:

provider: Dynatrace
config:
  url: <YOUR_DYNATRACE_URL>
  token: <YOUR_DYNATRACE_TOKEN>
  pushEvents: false

Then run:

akamas create telemetry-instance telemetry.yaml frontend

Study

In this live optimization:

the goal is to reduce the cost of the Kubernetes deployment. In this example, the cost is based on the amount of CPU and memory limits (assuming requests = limits).
the approval mode is set to manual, a new recommendation is generated daily
to avoid impacting application performance, constraints are specified on desired response times and error rates
to avoid impacting application reliability, constraints are specified on peak resource usage and out-of-memory kills
the parameters to be tuned are the container CPU and memory limits (we assume requests=limits in the deployment file)

Create a study.yaml manifest like the following:

name: frontend
system: frontend
workflow: frontend
requireApproval: true

goal:
  objective: minimize
  function:
    formula: (((container.container_cpu_limit/1000) * 3) + (container.container_memory_limit/(1024*1024*1024)))
  constraints:
    absolute:
      - name: Response Time
        formula: webapp.requests_response_time <= 300
      - name: Error Rate
        formula: webapp.service_error_rate:max <= 0.05
      - name: Container CPU saturation
        formula: container.container_cpu_util:p95 < 0.8
      - name: Container memory saturation
        formula: container.container_memory_util:max < 0.7
      - name: Container out-of-memory kills
        formula: container.container_oom_kills_count == 0

parametersSelection:
  - name: container.cpu_limit
    domain: [300, 1000]
  - name: container.memory_limit
    domain: [800, 1536]

windowing:
  type: trim
  trim: [5m, 0m]
  task: observe

workloadsSelection:
  - name: webapp.requests_throughput

steps:
  - name: baseline
    type: baseline
    numberOfTrials: 48
    values:
      container.cpu_limit: 1000
      container.memory_limit: 1536

  - name: optimize
    type: optimize
    numberOfTrials: 48
    numberOfExperiments: 100
    numberOfInitExperiments: 0
    maxFailedExperiments: 50

Then run:

akamas create study study.yaml

You can now follow the live optimization progress and explore the results using the Akamas UI for Live optimizations.

Optimizing cost of a Kubernetes microservice while preserving SLOs in production

In this example, you will use Akamas live optimization to minimize the cost of a Kubernetes deployment, while preserving application performance and reliability requirements.

Prerequisites

In this example, you need:

an Akamas instance
a Kubernetes cluster, with a deployment to be optimized
the kubectl command installed in the Akamas instance, configured to access the target Kubernetes and with privileges to get and update the deployment configurations
a supported telemetry data source (e.g. Prometheus or Dynatrace) configured to collect metrics from the target Kubernetes cluster

Optimization setup

Optimization packs

This example leverages the following optimization packs:

System

The system represents the Kubernetes deployment to be optimized (let's call it "frontend"). You can create a system.yaml manifest like this:

name: frontend
description: Kubernetes frontend deployment

Create the new system resource:

akamas create system system.yaml

The system will then have two components:

A Kubernetes container component, which contains container-level metrics like CPU usage and parameters to be tuned like CPU limits
A Web Application component, which contains service-level metrics like throughput and response time

Kubernetes component

Create a component-container.yaml manifest like the following:

name: container
description: Kubernetes container, part of the frontend deployment
componentType: Kubernetes Container
properties:
  dynatrace:
    type: CONTAINER_GROUP_INSTANCE
    kubernetes:
      namespace: boutique
      containerName: server
      basePodName: frontend-*

Then run:

akamas create component component-container.yaml frontend

Now create a component-webapp.yaml manifest like the following:

name: webapp
description: The service related to the frontend deployment
componentType: Web Application
properties:
  dynatrace:
    id: <TELEMETRY_DYNATRACE_WEBAPP_ID>

Then run:

akamas create component component-webapp.yaml frontend

Workflow

The workflow in this example is composed of three main steps:

Update the Kubernetes deployment manifest with the Akamas recommended deployment parameters (CPU and memory limits)
Apply the new parameters (kubectl apply)
Wait for the rollout to complete
Sleep for 30 minutes (observation interval)

Create a workflow.yaml manifest like the following:

name: frontend
tasks:
  - name: configure
    operator: FileConfigurator
    arguments:
      source:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
        path: frontend.yaml.templ
      target:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
        path: frontend.yaml

  - name: apply
    operator: Executor
    arguments:
      timeout: 5m
      host:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
      command: kubectl apply -f frontend.yaml

  - name: verify
    operator: Executor
    arguments:
      timeout: 5m
      host:
        hostname: mymachine
        username: user
        key: /home/user/.ssh/key
      command: kubectl rollout status --timeout=5m deployment/frontend -n boutique;

  - name: observe
    operator: Sleep
    arguments:
      seconds: 1800

Then run:

akamas create workflow workflow.yaml

Telemetry

Create the telemetry.yamlmanifest like the following:

provider: Dynatrace
config:
  url: <YOUR_DYNATRACE_URL>
  token: <YOUR_DYNATRACE_TOKEN>
  pushEvents: false

Then run:

akamas create telemetry-instance telemetry.yaml frontend

Study

In this live optimization:

the goal is to reduce the cost of the Kubernetes deployment. In this example, the cost is based on the amount of CPU and memory limits (assuming requests = limits).
the approval mode is set to manual, a new recommendation is generated daily
to avoid impacting application performance, constraints are specified on desired response times and error rates
to avoid impacting application reliability, constraints are specified on peak resource usage and out-of-memory kills
the parameters to be tuned are the container CPU and memory limits (we assume requests=limits in the deployment file)

Create a study.yaml manifest like the following:

name: frontend
system: frontend
workflow: frontend
requireApproval: true

goal:
  objective: minimize
  function:
    formula: (((container.container_cpu_limit/1000) * 3) + (container.container_memory_limit/(1024*1024*1024)))
  constraints:
    absolute:
      - name: Response Time
        formula: webapp.requests_response_time <= 300
      - name: Error Rate
        formula: webapp.service_error_rate:max <= 0.05
      - name: Container CPU saturation
        formula: container.container_cpu_util:p95 < 0.8
      - name: Container memory saturation
        formula: container.container_memory_util:max < 0.7
      - name: Container out-of-memory kills
        formula: container.container_oom_kills_count == 0

parametersSelection:
  - name: container.cpu_limit
    domain: [300, 1000]
  - name: container.memory_limit
    domain: [800, 1536]

windowing:
  type: trim
  trim: [5m, 0m]
  task: observe

workloadsSelection:
  - name: webapp.requests_throughput

steps:
  - name: baseline
    type: baseline
    numberOfTrials: 48
    values:
      container.cpu_limit: 1000
      container.memory_limit: 1536

  - name: optimize
    type: optimize
    numberOfTrials: 48
    numberOfExperiments: 100
    numberOfInitExperiments: 0
    maxFailedExperiments: 50

Then run:

akamas create study study.yaml

You can now follow the live optimization progress and explore the results using the Akamas UI for Live optimizations.