An Energy-Aware File Relocation Strategy Based on File-Access Frequency and Correlations

  • Cheng Hu
  • Yuhui DengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9531)


Energy consumption has become a big challenge of the traditional storage systems due to the explosive growth of data. A lot of research efforts have been invested in reducing the energy consumption of those systems. Traditionally, the frequently accessed data are concentrated into a small part of hot storage nodes, and other cold storage nodes are switched to a low-power state, thus saving energy. However, due to the energy penalty and time penalty, it takes extra energy and generates additional delay to switch a cold storage node from a low-power state to an active state. In contrast to the existing work, this paper proposes a Skew File Relocate (SFR) strategy which aggregates the correlated cold files to the same cold storage node in addition to concentrating the frequently accessed files to the hot nodes. Because the correlated files are normally accessed together, SFR can significantly reduce the number of power state transitions and lengthen the idle periods that the cold storage nodes are experienced, thus saving more energy and improving the system response time. Furthermore, other three relocation strategies are designed to explore the performance behavior of SFR. Experimental results demonstrate that SFR can significantly reduce the energy consumption while maintaining the system performance at an acceptable level.


Energy aware File relocation strategy File-access frequency File-access correlations Clustered storage system 



This work is supported by the National Natural Science Foundation (NSF) of China under Grant (No. 61572232, and No. 61272073), the key program of Natural Science Foundation of Guangdong Province (No.S2013020012865), the Open Research Fund of Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences (CARCH201401), and the Fundamental Research Funds for the Central Universities, and the Science and Technology Planning Project of Guangdong Province (No. 2013B090200021). And the corresponding author is Yuhui Deng from Jinan University.


  1. 1.
    Deng, Y.: What is the future of disk drives, death or rebirth? ACM Comput. Surv. 43(3), 1–27 (2011). Article 23CrossRefGoogle Scholar
  2. 2.
    R. Brown: Report to congress on server and data center energy efficiency: Public law 109–431. Lawrence Berkeley National Laboratory (2008)Google Scholar
  3. 3.
    Pinheiro, E., Bianchini, R., Carrera, E.V., Heath, T.: Dynamic cluster reconfiguration for power and performance. In: Benini, L., Kandemir, M., Ramanujam, J. (eds.) Compilers and Operating Systems for Low Power, pp. 75–93. Springer, New York (2003)CrossRefGoogle Scholar
  4. 4.
    Chase, J.S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., Doyle, R.P.: Managing energy and server resources in hosting centers. ACM SIGOPS Operating Syst. Rev. 35(5), 103–116 (2001). ACMCrossRefGoogle Scholar
  5. 5.
    Zhang, L., Deng, Y., Zhu, W., Peng, J., Wang, F.: Skewly replicating hot data to construct a power-efficient storage cluster. J. Netw. Comput. Appl. 50, 168–179 (2015). Elsevier ScienceCrossRefGoogle Scholar
  6. 6.
  7. 7.
    Cherkasova, L., Ciardo, G.: Characterizing temporal locality and its impact on web server performance. Technical Report HPL-2000-82, Hewlett Packard Laboratories (2000)Google Scholar
  8. 8.
    Xia, P., Feng, D., Jiang, H., Tian, L., Wang, F.: FARMER: a novel approach to file access correlations mining and evaluation reference model for optimizing peta-scale file system performance. In: Proceedings of the 17th International Symposium on High Performance Distributed Computing. ACM (2008)Google Scholar
  9. 9.
    Wu, Yi, Otagiri, Kenichi, Watanabe, Yousuke, Yokota, Haruo: A file search method based on intertask relationships derived from access frequency and RMC operations on files. In: Hameurlain, Abdelkader, Liddle, Stephen W., Schewe, Klaus-Dieter, Zhou, Xiaofang (eds.) DEXA 2011, Part I. LNCS, vol. 6860, pp. 364–378. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  10. 10.
    Verma, A., Ahuja, P., Neogi, A.: Power-aware dynamic placement of HPC applications. In: Proceedings of the 22nd Annual International Conference on Supercomputing. ACM (2008)Google Scholar
  11. 11.
    Bostoen, T., Mullender, S., Berbers, Y.: Power-reduction techniques for data-center storage systems. ACM Comput. Surv. (CSUR) 45(3) (2013). Article No. 33Google Scholar
  12. 12.
    Krioukov, A., et al.: NapSac: design and implementation of a power-proportional web cluster. ACM SIGCOMM Comput. Commun. Rev. 41(1), 102–108 (2011)CrossRefGoogle Scholar
  13. 13.
    Thereska, E., Donnelly, A., Narayanan, D.: Sierra: practical power-proportionality for data center storage. In: Proceedings of the Sixth Conference on Computer Systems. ACM (2011)Google Scholar
  14. 14.
    Deng, Y., Hu, Y., Meng, X., Zhu, Y., Zhang, Z., Han, J.: Predictively booting nodes to minimize performance degradation of a power-aware web cluster. Cluster Comput. 17(4), 1309–1322 (2014). Springer, New YorkCrossRefGoogle Scholar
  15. 15.
    Mashayekhy, L., Nejad, M., Grosu, D., Zhang, Q., Shi, W.: Energy-aware scheduling of mapreduce jobs for big data applications. IEEE Trans. Parallel Distrib. Syst. PP(99), 1 (2014)Google Scholar
  16. 16.
    Ebrahimirad, V., Goudarzi, M., Rajabi, A.: Energy-aware scheduling for precedence-constrained parallel virtual machines in virtualized data centers. J. Grid Comput. 13(2), 233–253 (2015)CrossRefGoogle Scholar
  17. 17.
    Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput., 1–20 (2015)Google Scholar
  18. 18.
    Weiser, M., Welch, B., Demers, A., Shenker, S.: Scheduling for reduced CPU energy. In: Imielinski, T., Korth, H.F. (eds.) Mobile Computing, pp. 449–471. Springer, New York (1996)CrossRefGoogle Scholar
  19. 19.
    Zikos, S., Karatza, H.D.: Performance and energy aware cluster-level scheduling of compute-intensive jobs with unknown service times. Simul. Model. Pract. Theory 19(1), 239–250 (2011)CrossRefGoogle Scholar
  20. 20.
    Patterson, D.A., Gibson, G., Katz, R.H.: A case for redundant arrays of inexpensive disks(RAID). In: Proceedings of the 1988 ACM SIGMOD International Conference on Management of Data, SIGMOD 1988, pp. 109–116. ACM, New York (1988)Google Scholar
  21. 21.
    Li, D., Wang, J.: EERAID: energy efficient redundant and inexpensive disk array. In: Proceedings of the 11th Workshop on ACM SIGOPS European Workshop, EW 11. ACM, New York (2004)Google Scholar
  22. 22.
    Weddle, C., et al.: PARAID: A gear-shifting power-aware RAID. ACM Trans. Storage (TOS) 3(3), 13 (2007)CrossRefGoogle Scholar
  23. 23.
    Mao, B., et al.: GRAID: A green RAID storage architecture with improved energy efficiency and reliability. In: 2008 IEEE International Symposium on Modeling, Analysis and Simulation of Computers and Telecommunication Systems, MASCOTS 2008. IEEE (2008)Google Scholar
  24. 24.
    Colarelli, D., Grunwald, D.: Massive arrays of idle disks for storage archives. In: Proceedings of the 2002 ACM/IEEE Conference on Supercomputing. IEEE Computer Society Press (2002)Google Scholar
  25. 25.
    Iritani, M., Yokota, H.: Effects on performance and energy reduction by file relocation based on file-access correlations. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops. ACM (2012)Google Scholar
  26. 26.
    Tait, C.D., Duchamp, D.: Detection and exploitation of file working sets. In: 11th International Conference on Distributed Computing Systems. IEEE (1991)Google Scholar
  27. 27.
    Lei, H., Duchamp, D.: An analytical approach to file prefetching. In: USENIX Annual Technical Conference (1997)Google Scholar
  28. 28.
    Kroeger, T.M., Long, D.D.E.: The case for efficient file access pattern modeling. In: Proceedings of the Seventh Workshop on Hot Topics in Operating Systems. IEEE (1999)Google Scholar
  29. 29.
    Kroeger, T.M., Long, D.D.E.: Design and implementation of a predictive file prefetching algorithm. In: USENIX Annual Technical Conference, General Track (2001)Google Scholar
  30. 30.
    Ishii, Y., Inaba, M., Hiraki, K.: Access map pattern matching for high performance data cache prefetch. J. Instr. Level Parallelism 13, 1–24 (2011)Google Scholar
  31. 31.
    He, J., Sun, X.H., Thakur, R.: Knowac: I/O prefetch via accumulated knowledge. In: 2012 IEEE International Conference on Cluster Computing (CLUSTER). IEEE (2012)Google Scholar
  32. 32.
    Jiang, S., Ding, X., Xu, Y., Davis, K.: A prefetching scheme exploiting both data layout and access history on disk. ACM Trans. Storage (TOS) 9(3), 10 (2013)Google Scholar
  33. 33.
    Agrawal, R., Imieliski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993). ACMCrossRefGoogle Scholar
  34. 34.
    Deng, Y.: Deconstructing network attached storage systems. J. Netw. Comput. Appl. 32(5), 1064–1072 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceJinan UniversityGuangzhouChina
  2. 2.State Key Laboratory of Computer ArchitectureInstitute of Computing, Chinese Academy of SciencesBeijingChina

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