Kube-Scheduler-Simulator: The Key to Better Scheduling in Kubernetes

The Kubernetes Scheduler is a crucial component of the Control Plane that determines which node a Pod will run on. Anyone using Kubernetes relies on a scheduler.

The kube-scheduler-simulator is a simulator for the Kubernetes Scheduler, initiated as a project in the Google Summer of Code 2021 and developed by me (Kensei Nakada). This application allows users to closely examine the behavior and decisions of the scheduler.

It is useful for casual users employing scheduling conditions (for example, Inter-Pod Affinities) as well as for experts extending the scheduler with custom plugins.

Motivation

The scheduler often appears as a black box composed of many plugins, each contributing to scheduling decisions from its own perspective. Understanding its behavior can be challenging, as it considers a multitude of factors.

Even if a Pod seems correctly scheduled in a simple test cluster, it might have been scheduled based on calculations different from what was expected. This discrepancy can lead to unexpected scheduling outcomes when deployed in a large production environment.

Moreover, testing a scheduler is a complex challenge. There are countless operational modes that run within a real cluster, making it impractical to foresee every scenario with a finite number of tests. Often, errors are only discovered when the scheduler is deployed in a real cluster. In fact, many bugs are found by users after release, even in the upstream kube-scheduler.

A development or sandbox environment for testing the scheduler—or indeed any Kubernetes controller—is a common practice. However, this approach falls short of capturing all potential scenarios that might occur in a production cluster, as a development cluster is often much smaller and exhibits significant differences in workload size and scaling dynamics. It never experiences the exact same usage or exhibits the same behavior as its production counterpart.

The kube-scheduler-simulator aims to solve these issues. It allows users to test their scheduling conditions, scheduler configurations, and custom plugins while reviewing every detailed aspect of scheduling decisions. Additionally, users can create a simulated cluster environment where they can test their scheduler with the same resources as in their production cluster without affecting actual workloads.

With ayedo as a Kubernetes partner, companies benefit from robust solutions to optimize their Kubernetes environments and effectively resolve scheduler issues.

Source: Kubernetes Blog