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Autonomic Databases: Detection of Workload Shifts with n-Gram-Models

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Advances in Databases and Information Systems (ADBIS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5207))

Abstract

Autonomic databases are intended to reduce the total cost of ownership for a database system by providing self-management functionality. The self-management decisions heavily depend on the database workload, as the workload influences both the physical design and the DBMS configuration. In particular, a database reconfiguration is required whenever there is a significant change, i.e. shift, in the workload.

In this paper we present an approach for continuous, light-weight workload monitoring in autonomic databases. Our concept is based on a workload model, which describes the typical workload of a particular DBS using n-Gram-Models. We show how this model can be used to detect significant workload changes. Additionally, a processing model for the instrumentation of the workload is proposed. We evaluate our approach using several workload shift scenarios.

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References

  1. Agrawal, S., et al.: Database tuning advisor for Microsoft SQL Server 2005. In: Proceedings of the 30th International Conference on Very Large Data Bases, pp. 1110–1121. Morgan Kaufmann, San Francisco (2004)

    Google Scholar 

  2. Dageville, B., Dias, K.: Oracle’s self-tuning architecture and solutions. IEEE Data Engineering Bulletin 29(3) (2006)

    Google Scholar 

  3. Ganik,, Corbi,: The dawning of the autonomic computing era. Ibm Systems Journal 42(1), 5–18 (2003)

    Article  Google Scholar 

  4. Dageville, B., et al.: Automatic SQL tuning in Oracle 10g. In: Proceedings of the 30th International Conference on Very Large Data Bases, pp. 1098–1109. Morgan Kaufmann, San Francisco (2004)

    Google Scholar 

  5. Zilio, D.C., et al.: Recommending materialized views and indexes with IBM DB2 design advisor. In: Proceedings of the International Conference on Autonomic Computing, pp. 180–188. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  6. Lahiri, T., et al.: The self-managing database: Automatic SGA memory management. White paper, Oracle Corporation (2003)

    Google Scholar 

  7. Storm, A.J., et al.: Adaptive self-tuning memory in DB2. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 1081–1092. ACM, New York (2006)

    Google Scholar 

  8. Holze, M., Ritter, N.: Towards workload shift detection and prediction for autonomic databases. In: Proceedings of the ACM first Ph.D. workshop in CIKM, pp. 109–116. ACM Press, New York (2007)

    Chapter  Google Scholar 

  9. Lightstone, S., et al.: Making DB2 products self-managing: Strategies and experiences. IEEE Data Engineering Bulletin 29(3) (2006)

    Google Scholar 

  10. Fink, G.: Markov Models for Pattern Recognition, 1st edn. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  11. Parekh, S., et al.: Throttling utilities in the IBM DB2 universal database server. In: Proceedings of the American Control Conference, pp. 1986–1991. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  12. Yao, Q., et al.: Finding and analyzing database user sessions. In: Zhou, L., Ooi, B.C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 851–862. Springer, Heidelberg (2005)

    Google Scholar 

  13. Katz, S.: Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech, and Signal Processing 35(3), 400–401 (1987)

    Article  Google Scholar 

  14. Jelinek, F., Mercer, R.L.: Interpolated estimation of markov source parameters from sparse data. In: Pattern Recognition in Practice, pp. 381–397. North-Holland, Amsterdam (1980)

    Google Scholar 

  15. Dell Laboratories: Dell DVD store database test suite (2008), http://linux.dell.com/dvdstore/

  16. Schnaitter, K., et al.: On-line index selection for shifting workloads. In: Proceedings of the 23rd International Conference on Data Engineering Workshops. IEEE Computer Society Press, Los Alamitos (2007)

    Google Scholar 

  17. Bruno, N., Chaudhuri, S.: An online approach to physical design tuning. In: Proceedings of the 23nd International Conference on Data Engineering, pp. 826–835. IEEE Computer Society Press, Los Alamitos (2007)

    Chapter  Google Scholar 

  18. Elnaffar, S., Martin, P., Horman, R.: Automatically classifying database workloads. In: Proceedings of the 2002 ACM CIKM International Conference on Information and Knowledge Management. ACM Press, New York (2002)

    Google Scholar 

  19. Martin, P., et al.: Workload models for autonomic database management systems. In: Proceedings of the International Conference on Autonomic and Autonomous Systems, p. 10. IEEE Computer Society, Los Alamitos (2006)

    Chapter  Google Scholar 

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Paolo Atzeni Albertas Caplinskas Hannu Jaakkola

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Holze, M., Ritter, N. (2008). Autonomic Databases: Detection of Workload Shifts with n-Gram-Models. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds) Advances in Databases and Information Systems. ADBIS 2008. Lecture Notes in Computer Science, vol 5207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85713-6_10

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  • DOI: https://doi.org/10.1007/978-3-540-85713-6_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85712-9

  • Online ISBN: 978-3-540-85713-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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