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-03] Optimize a Java-based application (Konakart) with LRE

[AIAB-03] Create the telemetry instances

Telemetry instances need to be created to allow Akamas to leverage data collected from LoadRunner Enterprise (web application metrics) and Prometheus (JVM and OS metrics).

Prometheus telemetry instance

The Prometheus telemetry instance collects metrics for a variety of technologies, including JVM and Linux OS metrics. Moreover, it can also be easily extended to import additional metrics (via custom promQL queries). In this example, you are going to use Prometheus to import JVM metrics exposed by the Prometheus JMX exporter.

First, update the tel_prometheus.yaml file replacing the target_host placeholder with the address of your Konakart instance:

provider: Prometheus
config:
  address: target_host
  port: 9090

And then create a telemetry instance associated with the konakart system:

akamas create telemetry-instance tel_prometheus.yaml konakart

LoadRunner Enterprise telemetry instance

As described in the LRE integration guide you need an instance of InfluxDB running in your environment to act as an external analysis server for your LRE instance. Therefore, the telemetry instance needs to provide all the configurations required to connect to that InfluxDB server.

The file tel_lre.yaml is an example of a LRE telemetry instance. Make sure to replace the variables with the actual values of your configurations:

provider: LoadRunnerEnterprise
config:
  address: http://target_host
  port: target_influx_port
  username: influx_user
  password: influx_user_password
  database: influx_database_schema

and then create telemetry instance:

akamas create telemetry-instance tel_lre.yaml konakart
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