Improving resource utilization by timely fine-grained scheduling

Abstract

Monotask is a unit of work that uses only a single type of resource (e.g., CPU, network, disk I/O). While monotask was primarily introduced as a means to reason about job performance, in this paper we show that this fine-grained, resource-oriented abstraction can be leveraged by job schedulers to maximize cluster resource utilization. Although recent cluster schedulers have significantly improved resource allocation, the utilization of the allocated resources is often not high due to inaccurate resource requests. In particular, we show that existing scheduling mechanisms are ineffective for handling jobs with dynamic resource usage, which exists in common workloads, and propose a resource negotiation mechanism between job schedulers and executors that makes use of monotasks. We design a new framework, called Ursa, which enables the scheduler to capture accurate resource demands dynamically from the execution runtime and to provide timely, fine-grained resource allocation based on monotasks. Ursa also enables high utilization of the allocated resources by the execution runtime. We show by experiments that Ursa is able to improve cluster resource utilization, which effectively translates to improved makespan and average JCT.