Quick Guides
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  • Quick Guides: Akamas in a sandbox
    • [AIAS-01] Guide: Explore an Optimization Study for a Kubernetes microservices application
      • [AIAS-01] Architecture overview
      • [AIAS-01] Explore the Study
      • [AIAS-01] Explore the System
      • [AIAS-01] Explore the Workflow
      • [AIAS-01] Explore the analysis
      • [AIAS-01] Explore the results
    • [AIAS-02] Guide: Create a study to optimize Java performance using the Akamas UI
      • [AIAS-02] Architecture overview
      • [AIAS-02] Create the Study
      • [AIAS-02] Define the optimization goal
      • [AIAS-02] Define the optimization parameters
      • [AIAS-02] Define the performance metrics
      • [AIAS-02] Define the optimization steps
      • [AIAS-02] Explore the results
    • [AIAS-03] Guide: Create a study to optimize K8s microservices costs using the Akamas CLI
      • [AIAS-03] Architecture overview
      • [AIAS-03] Create the system
      • [AIAS-03] Create the Workflow
      • [AIAS-03] Create the Study
      • [AIAS-03] Explore the results
  • Quick Guides: Akamas in a box
    • [AIAB-00] Install Akamas-in-a-box
      • [AIAB-00] Setup your Linux box
      • [AIAB-00] Install Akamas
    • [AIAB-01] Optimize a Java-based application (Renaissance benchmark)
      • [AIAB-01] Architecture overview
      • [AIAB-01] Create the System and its associated components
      • [AIAB-01] Configure the Telemetry
      • [AIAB-01] Create the workflow
      • [AIAB-01] Create and run the study
      • [AIAB-01] Explore the results
    • [AIAB-02] Optimize a Java-based application (Konakart) with JMeter
      • [AIAB-02] Architecture overview
      • [AIAB-02] Create the system and its components
      • [AIAB-02] Automate performance tests
      • [AIAB-02] Create the Telemetry Provider
      • [AIAB-02] Create the workflow
      • [AIAB-02] Create the study
      • [AIAB-02] Explore the results
    • [AIAB-03] Optimize a Java-based application (Konakart) with LRE
      • [AIAB-03] Architecture overview
      • [AIAB-03] Setup LoadRunner Enterprise
      • [AIAB-03] Create the system and its components
      • [AIAB-03] Create the telemetry instances
      • [AIAB-03] Create the workflow
      • [AIAB-03] Create the optimization study
    • [AIAB-04] Optimize a Java-based Kubernetes application (Online Boutique)
      • [AIAB-04] Architecture overview and setup
      • [AIAB-04] Setup Online Boutique
      • [AIAB-04] Setup Akamas
      • [AIAB-04] Create the system and its components
      • [AIAB-04] Create the workflow
      • [AIAB-04] Create the Study
      • [AIAB-04] Explore the results
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  1. Quick Guides: Akamas in a box
  2. [AIAB-01] Optimize a Java-based application (Renaissance benchmark)

[AIAB-01] Configure the Telemetry

In this section you will configure how Akamas collects metrics related to the renaissance system. Metrics are required both to define your optimization goal (e.g.: minimize the renaissance.response_time metric) and analyze the optimization results.

A Telemetry Provider specifies how to collect these metrics from a source, such as a monitoring platform (e.g. Prometheus or Dynatrace), a test tool (eg. Neoload or Loadrunner) or a simple CSV file. Akamas ships several out-of-the-box Telemetry Providers.

For each specific source, an instance of the corresponding Telemetry Provider needs to be defined at the system level.

Create a CSV telemetry instance

The output of the Renaissance benchmark suite is a CSV report file once the benchmark completes, which includes the benchmark execution time, CPU, and memory usage. Therefore, you will now create a CSV telemetry instance.

The file tel_csv.yaml provides the following definition:

provider: CSV File

config:
  protocol: scp
  address: benchmark
  username: akamas
  authType: password
  auth: akamas
  remoteFilePattern: renaissance-parsed.csv
  componentColumn: component
  timestampColumn: TS
  timestampFormat: yyyy-MM-dd HH:mm:ss zzz

metrics:
- metric: response_time
  datasourceMetric: duration_ns
- metric: cpu_used
  datasourceMetric: cpu_used
- metric: mem_used
  datasourceMetric: mem_used

Create the telemetry instance as follows:

akamas create telemetry-instance tel_csv.yaml renaissance

You can verify your new telemetry instance under the corresponding tab within the UI:

You can always list all telemetry instances in a system and verify that they have been correctly created:

akamas list telemetry-instances renaissance

So far you have defined how the application to be optimized looks like in terms of Akamas system and components, and the telemetry required to gather the relevant metrics. Your next step is to create a workflow, that is defining how to run optimization experiments.

Previous[AIAB-01] Create the System and its associated componentsNext[AIAB-01] Create the workflow

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