Local Resource Consumption Shaping: A Case for MapReduce

P. Lu*, Y. C. Lee, T. Ryan, V. Gramoli, A. Y. Zomaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Today, resource capacity is no longer an issue for running large-scale distributed systems, such as MapReduce. As a result, it is often the case that resources are provisioned, for such systems, at the level of peak loads. This overprovisioning has become a serious efficiency issue in cloud data centers with poor resource utilization. The improvement of resource utilization can be achieved by concurrently running tasks sharing a common set of resources. However, many distributed systems spawn a large number of tasks that exhibit similar resource consumption patterns causing much performance interference/degradation that is primarily due to fair resource sharing. In this study, we consider the problem of "local resource consumption shaping" - an alternative to fair resource sharing - at the local node/core level.

Original languageEnglish
Title of host publicationBig Data: Principles and Paradigms
EditorsRajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjredi
Place of PublicationCambridge, MA, USA
PublisherMorgan Kaufmann
Pages189-214
Number of pages26
ISBN (Electronic)9780128093467
ISBN (Print)9780128053942
DOIs
Publication statusPublished - 3 Jun 2016

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