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Making cluster applications energy-aware

Published:19 June 2009Publication History

ABSTRACT

Power consumption has become a critical issue in large scale clusters. Existing solutions for addressing the servers' energy consumption suggest "shrinking" the set of active machines, at least until the more power-proportional hardware devices become available. This paper demonstrates that leveraging the sleeping state, however, may lead to unacceptably poor performance and low data availability if the distributed services are not aware of the power management's actions. Therefore, we present an architecture for cluster services in which the deployed services overcome this problem by actively participating in any action taken by the power management. We propose, implement, and evaluate modifications for the Hadoop Distributed File System and the MapReduce clone that make them capable of operating efficiently under limited power budgets.

References

  1. Hadoop distributed file system.Google ScholarGoogle Scholar
  2. Luiz André Barroso and Urs Hölzle. The case for energy-proportional computing. Computer, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ricardo Bianchini and Ram Rajamony. Power and energy management for server systems. Computer, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Jeffrey S. Chase, Darrell C. Anderson, Prachi N. Thakar, Amin M. Vahdat, and Ronald P. Doyle. Managing energy and server resources in hosting centers. SIGOPS, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Fred Douglis, P. Krishnan, and Brian N. Bershad. Adaptive disk spin-down policies for mobile computers. In MLICS, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Mootaz Elnozahy, Michael Kistler, and Ramakrishnan Rajamony. Energy conservation policies for web servers. In USITS, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. U.S. EPA. Report to congress on server and data center energy efficiency. Technical report, 2007.Google ScholarGoogle Scholar
  8. Wu-chun Feng. Making a case for efficient supercomputing. Queue, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Krisztián Flautner, Steve Reinhardt, and Trevor Mudge. Automatic performance setting for dynamic voltage scaling. Wirel. Netw., 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The google file system. SIGOPS, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Sudhanva Gurumurthi, Anand Sivasubramaniam, Mahmut Kandemir, and Hubertus Franke. Drpm: dynamic speed control for power management in server class disks. SIGARCH, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Taliver Heath, Ana Paula Centeno, Pradeep George, Luiz Ramos, Yogesh Jaluria, and Ricardo Bianchini. Mercury and freon: temperature emulation and management for server systems. In ASPLOS, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kyong Hoon Kim, Rajkumar Buyya, and Jong Kim. Power aware scheduling of bag-of-tasks applications with deadline constraints on dvs-enabled clusters. In CCGRID, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Alvin R. Lebeck, Xiaobo Fan, Heng Zeng, and Carla Ellis. Power aware page allocation. SIGOPS, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Eduardo Pinheiro, Ricardo Bianchini, Enrique V. Carrera, and Taliver Heath. Dynamic cluster reconfiguration for power and performance. Compilers and operating systems for low power, pages 75--93, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ramya Raghavendra, Parthasarathy Ranganathan, Vanish Talwar, Zhikui Wang, and Xiaoyun Zhu. No "power" struggles: coordinated multi-level power management for the data center. SIGARCH, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Arun Rangasamy, Rahul Nagpal, and Y.N. Srikant. Compiler-directed frequency and voltage scaling for a multiple clock domain microarchitecture. In CF, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Alexey Rudenko, Peter Reiher, Gerald J. Popek, and Geoffrey H. Kuenning. Saving portable computer battery power through remote process execution. SIGMOBILE, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Yasushi Saito, Svend Frolund, Alistair Veitch, Arif Merchant, and Susan Spence. Fab: building distributed enterprise disk arrays from commodity components. SIGOPS, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Mark Weiser, Brent Welch, Alan Demers, and Scott Shenker. Scheduling for reduced cpu energy. In OSDI, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, and D. Stott Parker. Map-reduce-merge: simplified relational data processing on large clusters. In SIGMOD, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Matei Zaharia, Andy Konwinski, Anthony D. Joseph, Randy H. Katz, and Ion Stoica. Improving mapreduce performance in heterogeneous environments. In OSDI, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          ACDC '09: Proceedings of the 1st workshop on Automated control for datacenters and clouds
          June 2009
          64 pages
          ISBN:9781605585857
          DOI:10.1145/1555271

          Copyright © 2009 ACM

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          Publication History

          • Published: 19 June 2009

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