LaSA: A locality-aware scheduling algorithm for Hadoop-MapReduce resource assignment

Tseng Yi Chen, Hsin Wen Wei, Ming Feng Wei, Ying Jie Chen, Tsan Sheng Hsu, Wei Kuan Shih

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

Cloud computing has become more popular for a decade; it has been under continuous development with advances in architecture, software, and network. Hadoop-MapReduce is a common software framework processing parallelizable problem across big datasets using a distributed cluster of processors or stand-alone computers. Cloud Hadoop-MapReduce can scale incrementally in the number of processing nodes. Hence, the Hadoop-MapReduce is designed to provide a processing platform with powerful computation. Network traffic is always a most important bottleneck in data-intensive computing and network latency decreases significant performance in data parallel systems. Network bottleneck is caused by network bandwidth and the network speed is much slower than disk data access. So that, good data locality can reduces network traffic and increases performance in data-intensive HPC systems. However, Hadoop's scheduler has a defect of data locality in resource assignment. In this paper, we present a locality-aware scheduling algorithm (LaSA) for Hadoop-MapReduce scheduler. Firstly, we propose a mathematical model of weight of data interference in Hadoop scheduler. Secondly, we present the LaSA algorithm to use weight of data interference to provide data locality-aware resource assignment in Hadoop scheduler. Finally, we build an experimental environment with 3 cluster and 35 VMs to verify the LaSA's performance.

Original languageEnglish
Title of host publicationProceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013
Pages342-346
Number of pages5
DOIs
StatePublished - 2013
Event2013 International Conference on Collaboration Technologies and Systems, CTS 2013 - San Diego, CA, United States
Duration: 20 May 201324 May 2013

Publication series

NameProceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013

Conference

Conference2013 International Conference on Collaboration Technologies and Systems, CTS 2013
Country/TerritoryUnited States
CitySan Diego, CA
Period20/05/1324/05/13

Keywords

  • Cloud computing
  • data locality
  • distributed
  • hadoop
  • mapreduce

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