Data Prefetching Based on Long-Term Periodic Access Patterns

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)

Abstract

Data prefetching is a technique allowing to retrieve data which will be most likely needed in a near future, before the actual demand. Considerable research was devoted to this technique, however, it is typically based on short-term data access patterns. We propose to predict future accesses based on long-term periodic pattern mining. Human activity in many areas, and thus many real-world business processes appear to have natural periods: they may have day, week and/or month periods. Discovering of such periods in I/O (or higher level) activity logs should allow to build a prefetch predictor, which is aware of data accesses not only in a near future, but in a far future perspective as well, and thus able to make more reasonable prefetch decisions. In this work we investigate the algorithm for mining periodic long-term access patterns, and discuss issues involved in building a prefetch system, which integrates predictor based on discovering these patterns with a prefetch cost model.

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References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)Google Scholar
  2. 2.
    Amer, A., Long, D.D.E., Paris, J.-F., Burns, R.C.: File access prediction with adjustable accuracy. In: 21st IEEE International Proceedings of the Performance, Computing, and Communications Conference, PCC 2002, pp. 131–140. IEEE Computer Society, Washington, DC (2002)Google Scholar
  3. 3.
    Cao, P., Felten, E.W., Karlin, A.R., Li, K.: A study of integrated prefetching and caching strategies. In: Proceedings of the 1995 ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 1995/PERFORMANCE 1995, pp. 188–197. ACM, New York (1995)CrossRefGoogle Scholar
  4. 4.
    Griffioen, J., Appleton, R.: Reducing file system latency using a predictive approach. In: Proceedings of the USENIX Summer 1994 Technical Conference on USENIX Summer 1994 Technical Conference, USTC 1994, vol. 1, p. 13. USENIX Association, Berkeley (1994)Google Scholar
  5. 5.
    Kroeger, T.M., Long, D.D.E.: The case for efficient file access pattern modeling. In: Proceedings of the The Seventh Workshop on Hot Topics in Operating Systems, HOTOS 1999, p. 14. IEEE Computer Society, Washington, DC (1999)CrossRefGoogle Scholar
  6. 6.
    Li, Z., Chen, Z., Srinivasan, S.M., Zhou, Y.: C-miner: Mining block correlations in storage systems. In: Proceedings of the 3rd USENIX Conference on File and Storage Technologies, FAST 2004, pp. 173–186. USENIX Association, Berkeley (2004)Google Scholar
  7. 7.
    Patterson, R.H., Gibson, G.A., Ginting, E., Stodolsky, D., Zelenka, J.: Informed prefetching and caching. SIGOPS Oper. Syst. Rev. 29(5), 79–95 (1995)CrossRefGoogle Scholar
  8. 8.
    Yang, J., Wang, W., Yu, P.S.: Infominer: mining surprising periodic patterns. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 395–400. ACM, New York (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Saint Petersburg State UniversitySt.PetersburgRussia

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