NDLI: An effective data locality aware task scheduling method for MapReduce framework in heterogeneous environments

Content Provider	IEEE Xplore Digital Library
Author	Xiaohong Zhang Yuhong Feng Shengzhong Feng Jianping Fan Zhong Ming
Copyright Year	2011
Description	Author affiliation: School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China (Xiaohong Zhang; Jianping Fan) \|\| College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China (Yuhong Feng; Shengzhong Feng; Zhong Ming)
Abstract	Data locality has recently been extensively exploited in Cloud computing to improve system performance. However, when schedule Map tasks in Hadoop MapReduce framework working in a heterogeneous environment, existing methods either cannot reduce the occurrence of these Map tasks or injure fairness, thus degrading the system performance. In order to address this problem, this paper proposes a data locality aware scheduling method to improve the Hadoop MapReduce system performance in heterogeneous computing environments. After receiving a request from a requesting node, our method preferentially schedules the task whose input data is stored on the requesting node. If no such tasks exist, our method will select the task whose input data is nearest to the requesting node, and then make a decision on whether to reserve the task for the node storing the input data or schedule the task to the requesting node by transferring the input data to the requesting node on the fly. As a proof of concept, we implement the method in Hadoop-0.20.2. In order to evaluate the performance, we carry out an experimental comparison study on our proposed method against the default scheduling method used in Hadoop-0.20.2. The experiment results show that our proposed method improves the data locality and reduces the normalized execution time as well as the response time of jobs.
Starting Page	235
Ending Page	242
File Size	823515
Page Count	8
File Format	PDF
ISBN	9781457716355
e-ISBN	9781457716379
e-ISBN	9781457716362
DOI	10.1109/CSC.2011.6138527
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Schedules Cloud computing Processor scheduling Cloud Computing System performance Distributed databases Task Scheduling Distributed Computing Time factors Data communication Data Locality MapReduce
Content Type	Text
Resource Type	Article

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