Multi Resource Scheduling with Task Cloning in Heterogeneous Clusters

Abstract

To mitigate the straggler effect, today’s systems and computing frameworks have adopted redundancy to launch extra copies for stragglers. Two limitations of the existing straggler-mitigation techniques, however, are that resource demand of tasks is only considered in the context of slots and, moreover, redundancy is seldom coordinated with job scheduling. To tackle these issues, in this paper, we present DollyMP, a job scheduler that addresses multi-resource scheduling with task cloning in heterogeneous clusters. DollyMP carefully combines SRPT (Shortest Remaining Processing Time) and SVF (Smallest Volume First) via knapsack optimization to schedule tasks with multi-resource demands and, in the meanwhile, dynamically launches task clones to yield a small job completion time. DollyMP is built on a strong mathematical foundation to guarantee near-optimal performance. The deployment of our Hadoop YARN prototype on a 30-node cluster demonstrates that DollyMP can reduce job response time by 50% under different cluster loads.

Huanle Xu

2021.01 - Current

I am currently an assistant professor from the Department of Computer and Information Scicence, Univeristy of Macau.