Application-driven energy-efficient architecture explorations for big data

  • Authors:

  • Xiaoyan Gu;Rui Hou;Ke Zhang;Lixin Zhang;Weiping Wang

  • Affiliations:

  • Graduate University of Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 1st Workshop on Architectures and Systems for Big Data
  • Year:
  • 2011

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Abstract

Building energy-efficient systems is critical for big data applications. This paper investigates and compares the energy consumption and the execution time of a typical Hadoop-based big data application running on a traditional Xeon-based cluster and an Atom-based (Micro-server) cluster. Our experimental results show that the micro-server platform is more energy-efficient than the Xeon-based platform. Our experimental results also reveal that data compression and decompression accounts for a considerable percentage of the total execution time. More precisely, data compression/decompression occupies 7-11% of the execution time of the map tasks and 37.9-41.2% of the execution time of the reduce tasks. Based on our findings, we demonstrate the necessity of using a heterogeneous architecture for energy-efficient big data processing. The desired architecture takes the advantages of both micro-server processors and hardware compression/decompression accelerators. In addition, we propose a mechanism that enables the accelerators to perform more efficient data compression/decompression.