Tag: Series: Schedulers
14 Apr 2016 in Series: Schedulers, Schedulers, Kubernetes

Schedulers, Part 2: Kubernetes

In my previous post I introduced the concept of scheduling and took a look at two basic monolithic schedulers: fleet and swarm. In summary: schedulers are responsible for distributing jobs across a cluster of nodes. However, basic monolithic schedulers, by design, have limits on performance and throughput.

In this post we take a look at how Kubernetes improves on the basic monolithic design.

Intro to Kubernetes

Kubernetes is a tool for managing Linux containers across a cluster.

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1 Dec 2015 in Series: Schedulers, Docker, Fleet, Swarm, Overview

Schedulers, Part 1: Basic Monolithic

Scheduling is a method to to assign workloads to resources that can handle those workloads. In a distributed environments, there is a particularly important need for schedulers. Especially ones that provide scalability, are resource aware, and cost effective.

Monolithic schedulers are a single process entity that make scheduling decisions and deploy jobs to be scheduled. These jobs could be a long running server, a short living batch command, a MapReduce query, and so on.

For monolithic scheduler to make decision for scheduling a job, it should observe the resources available in the cluster (e.g. CPU, memory, and so on), lock the resources, schedule the job, and update the available resources.

It’s hard for Monolithic schedulers to deal with more than one job at a time because there is a single resource manager entity a single scheduling entity. Avoiding concurrency makes the system easier to design and easier to understand.

Some examples of monolithic schedulers:

  • fleet: a native scheduler to CoreOS (not resource aware)
  • Swarm: a scheduling backend for Docker containers
  • Kubernetes: an advanced type of monolithic scheduler for Pods (a collection of co-located containers that share same namespaces)
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