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Two Sides of a Coin: Optimizing the Schedule of MapReduce Jobs to Minimize Their Makespan and Improve Cluster Performance

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3 Author(s)
Verma, A. ; Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA ; Cherkasova, L. ; Campbell, R.H.

Large-scale MapReduce clusters that routinely process petabytes of unstructured and semi-structured data represent a new entity in the changing landscape of clouds. A key challenge is to increase the utilization of these MapReduce clusters. In this work, we consider a subset of the production workload that consists of MapReduce jobs with no dependencies. We observe that the order in which these jobs are executed can have a significant impact on their overall completion time and the cluster resource utilization. Our goal is to automate the design of a job schedule that minimizes the completion time (makespan) of such a set of MapReduce jobs. We offer a novel abstraction framework and a heuristic, called BalancedPools, that efficiently utilizes performance properties of MapReduce jobs in a given workload for constructing an optimized job schedule. Simulations performed over a realistic workload demonstrate that 15%-38% makespan improvements are achievable by simply processing the jobs in the right order.

Published in:

Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2012 IEEE 20th International Symposium on

Date of Conference:

7-9 Aug. 2012