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PB-LRU: a self-tuning power aware storage cache replacement algorithm for conserving disk energy

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Published:26 June 2004Publication History

ABSTRACT

Energy consumption is an important concern at data centers, where storage systems consume a significant fraction of the total energy. A recent study proposed power-aware storage cache management to provide more opportunities for the underlying disk power management scheme to save energy. However, the on-line algorithm proposed in that study requires cumbersome parameter tuning for each workload and is therefore difficult to apply to real systems.This paper presents a new power-aware on-line algorithm called PB-LRU (Partition-Based LRU) that requires little parameter tuning. Our results with both real system and synthetic workloads show that PB-LRU without any parameter tuning provides similar or even better performance and energy savings than the previous power-aware algorithm with the best parameter setting for each workload.

References

  1. Power, heat, and sledgehammer. White paper, Maximum Institution Inc., http://www.max-t.com/downloads/ whitepapers/ SledgehammerPowerHeat20411.pdf, 2002.]]Google ScholarGoogle Scholar
  2. P. Bohrer, E. N. Elnozahy, T. Keller, M. Kistler, C. Lefurgy, C. McDowell, and R. Rajamony. The case for power management in web servers. Power Aware Computing, Editors R. Graybill and R. Melhem, Klewer Academic Publishers, 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. E. V. Carrera, E. Pinheiro, and R. Bianchini. Conserving disk energy in network servers. In Proceedings of the 17th International Conference on Supercomputing, June 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Z. Chen, Y. Zhou, and K. Li. Eviction-based cache placement for storage caches. In Usenix Technical Conference, 2003.]]Google ScholarGoogle Scholar
  5. D. Colarelli and D. Grunwald. Massive arrays of idle disks for storage archives. In SC -- 2002, Nov 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. F. Douglis, R. Caceres, M. F. Kaashoek, K. Li, B. Marsh, and J. A. Tauber. Storage alternatives for mobile computers. In OSDI, pages 25--37, 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. F. Douglis, P. Krishnan, and B. Bershad. Adaptive disk spin-down policies for mobile computers. In Proc. 2nd USENIX Symp. on Mobile and Location-Independent Computing, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. F. Douglis, P. Krishnan, and B. Marsh. Thwarting the power-hungry disk. In USENIX Winter, pages 292--306, 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. E. N. Elnozahy, M. Kistler, and R. Rajamony. Energy-efficient server clusters. In the Second Workshop on Power Aware Computing Systems(held in conjunction with HPCA-2002), Feb 2002.]]Google ScholarGoogle Scholar
  10. EMC Corporation. Symmetrix 3000 and 5000 Enterprise Storage Systems product description guide. http://www.emc.com/products/product pdfs/pdg/symm_3_5_pdg.pdf, 1999.]]Google ScholarGoogle Scholar
  11. G. R. Ganger, B. L. Worthington, and Y. N. Patt. The DiskSim simulation environment - version 2.0 reference manual.]]Google ScholarGoogle Scholar
  12. C. Gniady, Y. C. Hu, and Y.-H. Lu. Program counter based techniques for dynamic power management. In 10th International Symposium on High Performance Computer Architecture, pages 24--35, Feb. 2004.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. R. A. Golding, P. B. II, C. Staelin, T. Sullivan, and J. Wilkes. Idleness is not sloth. In USENIX Winter, pages 201--212, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. P. Greenawalt. Modeling power management for hard disks. In the Conference on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Jan 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, and H. Franke. DRPM: Dynamic speed control for power management in server class disks. In Proceedings of the International Symposium on Computer Architecture, pages 169--179, June 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Gurumurthi, J. Zhang, A. Sivasubramaniam, M. Kandemir, H. Franke, N. Vijaykrishnan, and M. Irwin. Interplay of energy and performance for disk arrays running transaction processing workloads. In Proceedings of the International Symposium on Performance Analysis of Systems and Software (ISPASS), pages 123--132, Mar. 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. T. Heath, B. Diniz, E. V. Carrera, W. M. Jr., and R. Bianchini. Self-configuring heterogeneous server clusters. In COLP'03, Sept. 2003.]]Google ScholarGoogle Scholar
  18. T. Heath, E. Pinheiro, J. Hom, U. Kremer, and R. Bianchini. Application transformations for energy and performance-aware device management. In Proceedings of the 11th International Conference on Parallel Architectures and Compilation Techniques, Sept 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. P. Helmbold, D. D. E. Long, T. L. Sconyers, and B. Sherrod. Adaptive disk spin-down for mobile computers. Mobile Networks and Applications, 5(4):285--297, 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. D. Hill. Aspects of Cache Memory and Instruction Buffer Performance. PhD thesis, Unversity of Berkeley, 1987.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. D. Hill and A. J. Smith. Evaluating associativity in CPU caches. IEEE Transactions on Computers, 38(12), 1989.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. IBM. IBM Enterprise Storage Server. www.storage.ibm.com/hardsoft/products/ess/ess.htm IBM Corporation, 1999.]]Google ScholarGoogle Scholar
  23. S. Irani, S. Shukla, and R. Gupta. Competitive analysis of dynamic power management strategies for systems with multiple power saving states. Technical report, UCI-ICS, Sept 2001.]]Google ScholarGoogle Scholar
  24. T. Johnson and D. Shasha. 2Q: A low overhead high performance buffer management replacement algorithm. In J. Bocca, M. Jarke, and C. Zaniolo, editors, VLDB, pages 439--450, Los Altos, CA 94022, USA, 1995. Morgan Kaufmann Publishers.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. J. Kim, J. Choi, J. Kim, S. Noh, S. Min, Y. Cho, and C. Kim. A low-overhead high-performance unified buffer management scheme that exploits sequential and looping references. OSDI, 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. P. Krishnan, P. M. Long, and J. S. Vitter. Adaptive disk spindown via optimal rent-to-buy in probabilistic environments. In 12th International Conference on Machine Learning, 1995.]]Google ScholarGoogle Scholar
  27. S. T. Leutenegger and D. Dias. A modeling study of the TPC-C benchmark. SIGMOD Record, 22(2):22--31, June 1993.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. K. Li, R.Kumpf, P.Horton, and T.E. Anderson. A quantitative analysis of disk drive power management in portable computers. In USENIX Winter, 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Y.-H. Lu and G.D. Micheli. Comparing system-level power management policies. IEEE Design and Test of Computers, 18(2):10--19, March 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. S.Martello and P.Toth. Knapsack problems: Algorithms and computer implementations. John Wiley and Sons, Ltd., 1990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. R.L. Mattson, J.Gecsei, D.R. Slutz, and I.L. Traiger. Evaluation techniques for storage hierarchies. IBM Systems Journal, 9(2):78--117, 1970.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. N.Megiddo and D.S. Modha. Arc: A self-tuning, low overhead replacement cache. In FAST'03, 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. B.Moore. Taking the data center power and cooling challenge. Energy User News, August 27th, 2002.]]Google ScholarGoogle Scholar
  34. F.Moore. More power needed. Energy User News, Nov 25th, 2002.]]Google ScholarGoogle Scholar
  35. A. E. Papathanasiou and M. L. Scott. Increasing disk burstiness for energy efficiency. Technical Report 792, University of Rochester, November 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. R. H. Patterson, G. A. Gibson, E. Ginting, D. Stodolsky, and J. Zelenka. Informed prefetching and caching. In the 15th ACM Symposium on Operating System Principles, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. E. Pinheiro and R. Bianchini. Energy conservation techniques for disk array-based servers. In the 18th International Conference on Supercomputing, June 2004.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. E. Pinheiro, R. Bianchini, E. V. Carrera, and T. Heath. Load balancing and unbalancing for power and performance in cluster-based systems. COLP'01, 2001.]]Google ScholarGoogle Scholar
  39. W. H. Wang and J. L. Baer. Efficient trace-driven simulation method for cache performance analysis. In SIGMETRICS, 1990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. A. Weissel, B. Beutel, and F. Bellosa. Cooperative I/O: A novel I/O semantics for energy-aware applications. In OSDI, Dec. 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. T. Wong and J. Wilkes. My cache or yours? making storage more exclusive. In USENIX Annual Technical Conference (USENIX), 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. J. Zedlewski, S. Sobti, N. Garg, A. Krishnamurthy, and R. Wang. Modeling hard-disk power consumption. In the 2nd USENIX Conference on File and Storage Technologies, 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Y. Zhou, A. Bilas, S. Jagannathan, C. Dubnicki, J. F. Philbin, and K. Li. Experiences with VI communication for database storage. In ISCA'02, May 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Y. Zhou, J. F. Philbin, and K. Li. The multi-queue replacement algorithm for second level buffer caches. In Proceedings of the Usenix Technical Conference, June 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Q. Zhu, F. M. David, C. F. Devaraj, Z. Li, Y. Zhou, and P. Cao. Reducing energy consumption of disk storage using power-aware cache management. In 10th International Symposium on High Performance Computer Architecture, 2004.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      ICS '04: Proceedings of the 18th annual international conference on Supercomputing
      June 2004
      360 pages
      ISBN:1581138393
      DOI:10.1145/1006209

      Copyright © 2004 ACM

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      Publication History

      • Published: 26 June 2004

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