Communications of the ACM
Designing Cluster Schedulers for Internet-Scale Services
In the past decade, cluster schedulers have become the foundation of Internet-scale services that span hundreds or thousands of machines across many geographically distributed datacenters. Cluster schedulers have their origins in high-performance computing or managing services that run on warehouse-scale computing systems, which allow for efficient usage of computational resources. Schedulers also provide an API for application developers writing distributed applications such as Spark and MapReduce or building application management platforms such as DC/OS (Data Operating System). In the past few years open source schedulers such as Kubernetes, HashiCorp's Nomad, and Apache Mesos have democratized scale, allowing many enterprises to adopt scheduler technologies that were previously accessible only to companies like Facebook, Google, and Twitter.
Despite this apparent ubiquity, operating and implementing scheduling software is an exceedingly tricky task with many nuanced edge cases. This article highlights some of these cases based on the real-world experience of the authors designing, building, and operating a variety of schedulers for large Internet companies.
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