Peloton
A unified resource scheduler for co-scheduling batch, stateless, and stateful workloads in a single cluster to maximize resource utilization.
What is Peloton?
Peloton is a unified resource scheduler that co-schedules mixed workloads—such as batch, stateless, and stateful jobs—in a single compute cluster. It solves the problem of low resource utilization in large-scale environments by enabling elastic resource sharing, overcommitment, and workload preemption. Designed for web-scale operations, it handles millions of containers and tens of thousands of nodes efficiently.
Platform engineers, SREs, and infrastructure teams at web-scale companies managing large, heterogeneous clusters with diverse workload types. It is particularly relevant for organizations running big data and machine learning workloads alongside traditional services.
Developers choose Peloton for its ability to maximize cluster utilization through advanced features like hierarchical resource pools, GPU/gang scheduling, and cloud-agnostic deployment. Its scalability and support for mixed workloads make it a robust alternative to simpler schedulers in complex, high-demand environments.
Overview
Unified Resource Scheduler to co-schedule mixed types of workloads such as batch, stateless and stateful jobs in a single cluster for better resource utilization.
Use Cases
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