NDLI: Performance and energy efficiency of big data systems: characterization, implication and improvement

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Performance and energy efficiency of big data systems: characterization, implication and improvement

Content Provider	ACM Digital Library
Author	Shi, Yingjie Wang, Lei Du, Fang
Abstract	Large volume of data is produced by various applications in the world, processing such scale of data has great challenges in not only performance but also energy efficiency. Researchers propose various techniques to either improve the performance or the energy efficiency. The techniques of these two trends, however, are significantly different. When both performance and energy efficiency are concerned in the big data systems, how to get balance has become an issuing and challenging problem for data center administrators and hardware designers. In this paper, we conduct comprehensive evaluations on two representative platforms with different types of processors. We quantify the performance and energy efficiency, relating the evaluation results to micro-architectural activities and application characteristics. Two interesting findings are made from our evaluations: (1) the performance and energy efficiency are not only determined by the hardware technology, but also associated with the application characteristics; (2) there is no ever-victorious microprocessor in terms of both performance and energy efficiency in all the big data workloads. Based on the findings and quantified evaluation results, we provide great guidance and implications for both data center administrators and big data system designers, and we argue that a hybrid-core is an efficient way to improve the energy efficiency of big data systems with minimum performance degradation.
Starting Page	55
Ending Page	61
Page Count	7
File Format	PDF
ISBN	9781450348577
DOI	10.1145/3056662.3056667
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Big data systems Hybrid core Energy efficiency Performance
Content Type	Text
Resource Type	Article

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