Open Sourcing Peloton, Uber’s Unified Resource Scheduler

First introduced by Uber in November 2018, Peloton, a unified resource scheduler, manages resources across distinct workloads, combining separate compute clusters. Peloton is designed for web-scale companies like Uber with millions of containers and tens of thousands of nodes. Peloton features advanced resource management capabilities such as elastic resource sharing, hierarchical max-min fairness, resource overcommits, and workload preemption. As a cloud-agnostic system, Peloton can be run in on-premise data centers or in the cloud.

At Uber, Peloton is a critical piece of infrastructure powering our compute clusters. It is currently running many kinds of batch workloads in production, and we are planning to migrate stateless services workloads to it as well.

Today, Uber is excited to announce that we are open sourcing Peloton. By allowing others in the cluster management community to leverage unified schedulers and workload co-location, Peloton will open the door for more efficient resource utilization and management across the community. Moreover, open sourcing Peloton will enable greater industry collaboration and open up the software to feedback and contributions from industry engineers, independent developers, and academics across the world.

To our knowledge, there is no other open source scheduler which combines all types of workloads for web-scale companies like Uber. Prior to Peloton, each workload at Uber had its own cluster, resulting in many inefficiencies. With Peloton, we can colocate mixed workloads in shared clusters for better resource utilization.

As shown by the Google Borg paper, co-locating diverse workloads on shared clusters is key to improving cluster utilization and reducing overall cluster cost. Below, we outline some examples of how co-locating mixed workloads will drive utilization in our compute clusters, as well as helps us more accurately plan cluster provisioning:

Realizing that these scenarios would enable us to achieve greater operational efficiency, improve capacity planning, and optimize resource sharing, it was evident that we needed to co-locate different workloads together on one single, shared compute platform. A unified resource scheduler will enable us to manage all kinds of workloads to use our resources as efficiently as possible both in private data centers and the cloud.

Peloton will support Uber’s workloads with a single, shared platform, balancing resource usage by elastically sharing resources, and helping teams better plan for future capacity needs. Learn more about these benefits by reading our recent article on Peloton.

Uber has been running Peloton in production for more than a year and it’s scaling and running very well. Below are some of the feature highlights.

Uber’s Peloton team is also working on stateless service support, coming soon. Please visit the Next Steps section of our article on Peloton for more details.

We hope you try out Peloton for yourself! Learn more by reading our recent article on Peloton and our documentation or joining our Slack channel with any questions about the software.