Creating telemetry instances
Last updated
Last updated
After modeling the system and its components, the following step (see the following figure) is to ensure that all the metrics that are required to define goals and constraints and analyze the behavior of the target system can be collected from one of the available data sources available in the environment, that in Akamas are called telemetry providers.
Akamas provides a number of out-of-the-box telemetry providers, including industry-standard monitoring platforms (e.g. Prometheus or Dynatrace), performance testing tools (e.g. LoadRunner or JMeter), or simple CSV files. The section Integrating Telemetry Providers lists all the out-of-the-box telemetry providers and how to get them integrated by Akamas, while the section Telemetry metric mapping describes the mapping of the specific data source metrics to Akamas metrics).
Since several instances of a data source type might be available, the specific data source instance needs to be specified, that is a corresponding telemetry instance needs to be defined for the modeled system and its components.
The Telemetry instance template section of the reference guide describes the template required to define a telemetry instance, while the commands for creating a telemetry instance are listed on the Resource Management command page.
Telemetry Providers are shared across all the workspace in the same Akamas installation and require an account with administrative privileges to manage them. Any number of telemetry instances (even of the same type) can be specified. For example, the following figure shows two Prometheus telemetry instances associated with the Adservice system.
The following sections provide guidelines on how to create telemetry instances.
Verify metrics provided by the telemetry provider
A seemingly obvious, yet fundamental, best practice when choosing a telemetry provider is to check whether the required metrics:
are supported by the original data source or can be added (e.g. as it is in the case of Prometheus)
are available and can be effectively gathered in the specific implementation
are supported by the telemetry provider itself or whether it needs to be extended (this is the case for a Prometheus telemetry provider ) as in the case of custom metrics such as those made available by the application itself
Akamas makes it possible to validate whether a telemetry setup works correctly by first executing dry runs. This is discussed in the context of the recommended practices to run optimization studies (section Running optimization studies).
