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Importance of Metrics Analysis for Containers

By Logistics Tech Outlook | Tuesday, January 08, 2019

Modern computing solutions are largely based on containers. In the traditional mainframe computing solutions, the analysis and collection of metrics was a simple process, as it was easy for developers to determine the memory or storage issues.

Enterprises have switched from hardware style of computing to a virtual computing solution. The virtual computing solutions are complex computing solutions that require continuous coordination and management for efficient services. Containers have become widely popular for its innovative and efficient services. Containers provide a scalability feature, which makes it easy to be added or subtracted from an environment. Application developers use Kubernetes to manage their containers by putting the containers into pods. These pods are then placed onto nodes to balance the loads. Containers also allow the developers to release or kill the pod according to the requirement. Containers provide very dynamic and ephemeral processes, which makes it tough to maintain its visibility.

Metrics analysis and calculation is an essential step in managing the dynamic container environment. It can pinpoint the reason for an application’s failure, be it a node failure, or a problem pod, or a problem with Docker container. Docker containers and kubernetes provide metrics APIs. For example, GitHub provides a partial list of pod metrics. Enterprises need to decide on the type of metrics required for the business. For example, for a pod doing an HTTP listener, the developers might look for an uptime measurement; or if a pod is handling a credit check service, the metrics for response time and the queue of people waiting on the service might be required. The metrics measurement is not only crucial to the orchestrator managing resources, but it can also be tied to the business SLA.

The information for time series measurement creates an increased instrumentation workload for microservices, pods, containers, and nodes. This makes metric storage an issue for organizations. Many tools provide metric storage facility in a container on the node cluster, but it is not a secure option as information can be lost once the node goes down. Enterprises should focus on the metric stores that are purpose-built time series databases. The metric store should also be able to orchestrate the environment and provide thorough visibility of the environment.

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