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PRE-BUD: Prefetching for energy-efficient parallel I/O systems with buffer disks

Published:27 June 2011Publication History
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Abstract

A critical problem with parallel I/O systems is the fact that disks consume a significant amount of energy. To design economically attractive and environmentally friendly parallel I/O systems, we propose an energy-aware prefetching strategy (PRE-BUD) for parallel I/O systems with disk buffers. We introduce a new architecture that provides significant energy savings for parallel I/O systems using buffer disks while maintaining high performance. There are two buffer disk configurations: (1) adding an extra buffer disk to accommodate prefetched data, and (2) utilizing an existing disk as the buffer disk. PRE-BUD is not only able to reduce the number of power-state transitions, but also to increase the length and number of standby periods. As such, PRE-BUD conserves energy by keeping data disks in the standby state for increased periods of time. Compared with the first prefetching configuration, the second configuration lowers the capacity of the parallel disk system. However, the second configuration is more cost-effective and energy-efficient than the first one. Finally, we quantitatively compare PRE-BUD with both disk configurations against three existing strategies. Empirical results show that PRE-BUD is able to reduce energy dissipation in parallel disk systems by up to 50 percent when compared against a non-energy aware approach. Similarly, our strategy is capable of conserving up to 30 percent energy when compared to the dynamic power management technique.

References

  1. Benini, L., Bogliolo, A., and De Micheli, G. 2000. A survey of design techniques for system-level dynamic power management. IEEE Trans. VLSI Syst. 8, 299--316. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Carrera, E. V., Pinheiro, E., and Bianchini, R. 2003. Conserving disk energy in network servers. In Proceedings of the 17th International Conference on Supercomputing. 86--97. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Chen, F., Jiang, S., Shi, W., and Yu, W. 2007. Flexfetch: A history-aware scheme for I/O energy saving in mobile computing. In Proceedings of the International Conference on Parallel Processing (ICPP'07). IEEE Computer Society, 10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Colarelli, D. and Grunwald, D. 2002. Massive arrays of idle disks for storage archives. In Proceedings of the ACM/IEEE Conference on Supercomputing (Supercomputing'02). IEEE Computer Society Press, Los Alamitos, CA, 1--11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Douglis, F., Krishnan, P., and Marsh, B. 1994. Thwarting the power-hungry disk. In Proceedings of the Winter USENIX Conference. 293--306. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Eom, H. and Hollingsworth, J. K. 2000. Speed vs. accuracy in simulation for I/O-intensive applications. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE Computer Society Press, 315--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Gurumurthi, S., Sivasubramaniam, A., Kandemir, M., and Franke, H. 2003. DRPM: Dynamic speed control for power management in server class disks. In Proceedings of the 30th Annual International Symposium on Computer Architecture. 169--179. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Hawkins, J., and Bodn, M. 2005. The applicability of recurrent neural networks for biological sequence analysis. IEEE/ACM Trans. Computat. Biol. Bioinf. 2, 243--253. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Helmbold, D., Long, D., Sconyers, T., and Sherrod, B. 1998. Adaptive disk spin-down for mobile computers. Mobile Netw. Appl. 5, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Jones, E. 2006. EPA announces new computer efficiency requirements. EPA Anouncement.Google ScholarGoogle Scholar
  11. Kallahalla, M. and Varman, P. J. 2002. PC-OPT: Optimal offline prefetching and caching for parallel I/O systems. IEEE Trans. Comput. 51, 11, 1333--1344. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kim, Y.-J., Kwon, K.-T., and Kim, J. 2007. Energy-efficient disk replacement and file placement techniques for mobile systems with hard disks. In Proceedings of the ACM Symposium on Applied Computing (SAC'07). ACM, New York, 693--698. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Krishnan, P., Long, P. M., and Vitter, J. S. 1995. Adaptive disk spindown via optimal rent-to-buy in probabilistic environments. Tech. rep., Duke University. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Kwan, T. T., McGrath, R. E., and Reed, D. A. 1995. NCSA's world wide web server: Design and performance. IEEE Comput. 28, 68--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Li, K., Kumpf, R., Horton, P., and Anderson, T. 1994. A quantitative analysis of disk drive power management in portable computers. In Proceedings of the Winter USENIX Conference. 279--291. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Maximum Throughput, Inc. 2002. Power, heat, and sledgehammer. White Paper.Google ScholarGoogle Scholar
  17. Moore B. 2002. Taking the data center power and cooling challenge. Energy User News.Google ScholarGoogle Scholar
  18. Papathanasiou, A. E. and Scott, M. L. 2004. Energy efficient prefetching and caching. In Proceedings of the Annual Conference on USENIX Annual Technical Conference (ATEC'04). USENIX Association, Berkeley, CA, USA, 22--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Pinheiro, E. and Bianchini, R. 2004. Energy conservation techniques for disk array-based servers. In Proceedings of the 18th Annual International Conference on Supercomputing (ICS'04). ACM, New York, 68--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Shen, H., Kumar, M., Das, S., and Wang, Z. 2004. Energy-efficient caching and prefetching with data consistency in mobile distributed systems. In Proceedings of the 18th International Symposium on Parallel and Distributed Processing. 67.Google ScholarGoogle Scholar
  21. Son, S. W. and Kandemir, M. T. 2006. Energy-aware data prefetching for multi-speed disks. In Proceedings of the Conference on Computing Frontiers. 105--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Trizna, D. 2005. Microwave and hf multi-frequency radars for dual-use coastal remote sensing applications. In Proceedings of MTS/IEEE (OCEANS'05). 1, 532--537.Google ScholarGoogle ScholarCross RefCross Ref
  23. Wang, J., Zhu, H., and Li, D. 2008. ERAID: Conserving energy in conventional disk-based raid system. IEEE Trans. Comput. 57, 3, 359--374. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Xie, T. 2008. SEA: A striping-based energy-aware strategy for data placement in raid-structured storage systems. IEEE Trans. Comput. 57, 6, 748--761. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Zhu, Q., Chen, Z., Tan, L., Zhou, Y., Keeton, K., and Wilkes, J. 2005. Hibernator: Helping disk arrays sleep through the winter. SIGOPS Oper. Syst. Rev. 39, 5, 177--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Zhu, Q., David, F. M., Devaraj, C. F., Li, Z., Zhou, Y., and Cao, P. 2004a. Reducing energy consumption of disk storage using power-aware cache management. In Proceedings of the 10th International Symposium on High Performance Computer Architecture. IEEE Computer Society, 118. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Zhu, Q., Shankar, A., and Zhou, Y. 2004b. PB-LRU: A self-tuning power aware storage cache replacement algorithm for conserving disk energy. In Proceedings of the 18th Annual International Conference on Supercomputing (ICS'04). ACM, New York, 79--88. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Zhuang, X. and Pande, S. 2004. Power-efficient prefetching via bit-differential offset assignment on embedded processors. In Proceedings of the ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES'04). ACM, New York, 67--77. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Zong, Z., Briggs, M., O'Connor, N., and Qin, X. 2007. An energy-efficient framework for large-scale parallel storage systems. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDS'07). 1--7.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Transactions on Storage
        ACM Transactions on Storage  Volume 7, Issue 1
        June 2011
        73 pages
        ISSN:1553-3077
        EISSN:1553-3093
        DOI:10.1145/1970343
        Issue’s Table of Contents

        Copyright © 2011 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 27 June 2011
        • Accepted: 1 November 2010
        • Revised: 1 October 2010
        • Received: 1 April 2010
        Published in tos Volume 7, Issue 1

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