NDLI: Improving Resource Utilization in MapReduce

Content Provider	IEEE Xplore Digital Library
Author	Zhenhua Guo Fox, G. Mo Zhou Yang Ruan
Copyright Year	2012
Abstract	MapReduce has been adopted widely in both academia and industry to run large-scale data parallel applications. In MapReduce, each slave node hosts a number of task slots to which tasks can be assigned. So they limit the maximum number of tasks that can execute concurrently on each node. When all task slots of a node are not used, the resources âreservedâ for idle slots are unutilized. To improve resource utilization, we propose resource stealing to enable running tasks to steal resources reserved for idle slots and give them back proportionally whenever new tasks are assigned. Resource stealing makes the otherwise wasted resources get fully utilized without interfering with normal job scheduling. MapReduce uses speculative execution to improve fault tolerance. Current Hadoop implementation decides whether to run speculative tasks based on the progress rates of running tasks, which does not take into consideration the absolute progress of each task. We propose Benefit Aware Speculative Execution which evaluates the potential benefit of speculative tasks and eliminates unnecessary runs. We implement the proposed algorithms in Hadoop, and our experiments show that our algorithms can significantly shorten job execution time and reduce the number of non-beneficial speculative tasks.
Starting Page	402
Ending Page	410
File Size	265205
Page Count	9
File Format	PDF
ISBN	9781467324229
e-ISBN	9780769548074
DOI	10.1109/CLUSTER.2012.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Resource management Radio frequency Peer to peer computing Nickel Energy consumption Hardware Parallel processing speculative execution MapReduce scheduling utilization
Content Type	Text
Resource Type	Article

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