NDLI: Improving MapReduce Performance by Streaming Input Data from Multiple Replicas

Content Provider	IEEE Xplore Digital Library
Author	Jiadong Wu Bo Hong
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Jiadong Wu; Bo Hong)
Abstract	The MapReduce programming model, along with its open-source implementation Hadoop has provided a cost effective solution for many data-intensive applications. Hadoop stores data distributively and exploits data locality by assigning tasks to where data is stored. In many cases, however, accessing remote data (rack-local and off-rack) is inevitable. In this paper we are evaluating the possibility of improving the remote data accessing performance by streaming data from multiple available replicas. The proposed design consists of a circular buffer, a slice reader and a enhanced Data Node. Such system is capable of adapting to both the static performance variance caused by network topology as well as dynamic variance caused by congestion. Extensive experiments show that mutil-source streaming can significantly improve the throughput of remote data access and accelerate the related map tasks by 10%-20%. In some imbalanced environment, the proposed system can even achieve as much as 4x speedup.
Starting Page	623
Ending Page	630
File Size	291600
Page Count	8
File Format	PDF
ISBN	9780769550954
DOI	10.1109/CloudCom.2013.88
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-02
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Streaming Network topology Bandwidth Hadoop Benchmark testing Media Throughput Peer-to-peer computing Servers Mutil-source MapReduce
Content Type	Text
Resource Type	Article

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