Hopper: Decentralized Speculation-aware Cluster Scheduling at Scale

By Xiaoqi Ren, Ganesh Ananthanarayanan, Adam Wierman, Minlan Yu. 2015

151 citations recorded by the source corpus.

As clusters continue to grow in size and complexity, providing scalable and predictable performance is an increasingly important challenge. A crucial roadblock to achieving predictable performance is stragglers, i.e., tasks that take significantly longer than expected to run. At this point, speculative execution has been widely adopted to mitigate the impact of stragglers. However, speculation mechanisms are designed and operated independently of job scheduling when, in fact, scheduling a speculative copy of a task has a direct impact on the resources available for other jobs. In this work, we present Hopper, a job scheduler that is speculation-aware, i.e., that integrates the tradeoffs associated with speculation into job scheduling decisions. We implement both centralized and decentralized prototypes of the Hopper scheduler and show that 50% (66%) improvements over state-of-the-art centralized (decentralized) schedulers and speculation strategies can be achieved through the coordination of scheduling and speculation.

Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems, ML Systems & Hardware

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Citations in the corpus

Pathways: Asynchronous Distributed Dataflow for ML (2022, background): “…optimize for heterogeneity of ML work-loads (Narayanan et al., 2020) and multi-job, multi-user fairness and performance (Xiao et al., 2018; Ren et al., 2015; Mahajan et al., 2020; Jeon et al., 2018), but resources are still exclusively dedicated to single jobs at long time scales (seconds…”
Computation Offloading Toward Edge Computing (2019, background): “Furthermore, Hopper [120] extends the basic technologies of Sparrow and further provides scalability and predictability in task scheduling.”