A PREDICTIVE AUTOMATIC TUNING SERVICE FOR OBJECT POOLING BASED ON DYNAMIC MARKOV MODELING

Nima Sharifimehr, Samira Sadaoui

2007

Abstract

One of the most challenging concerns in the development of enterprise software systems is how to manage effectively and efficiently available resources. Object pooling service as a resource management facility significantly improves the performance of application servers. However, tuning object pool services is a complicated task that we address here through a predictive automatic approach. Based on dynamic markov models, which capture high-order temporal dependencies and locally optimize the required length of memory, we find patterns across object invocations that can be used for prediction purposes. Subsequently, we propose an effective automatic tuning solution, with reasonable time costs, which takes advantage of past and future information about activities of object pool services. Afterwards, we present experimental results which demonstrate the scalability and effectiveness of our novel tuning solution, namely predictive automatic tuning service.

References

  1. Alur, D., Malks, D., and Crupi, J. (2001). Core J2EE Patterns: Best Practices and Design Strategies. Prentice Hall PTR, Upper Saddle River, NJ, USA.
  2. Anderson, P. and Anderson, G. (2002). Enterprise JavaBeans Components Architecture: Designing and Coding Enterprise Applications. Prentice Hall Professional Technical Reference.
  3. Barrera, J. S. (1993). Self-tuning systems software. In Proc. Fourth Workshop on Workstation Operating Systems, pages 194-197.
  4. Cecchet, E., Marguerite, J., and Zwaenepoel, W. (2002). Performance and scalability of ejb applications. In OOPSLA 7802: Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications, pages 246-261, New York, NY, USA. ACM Press.
  5. Cormack, G. V. and Horspool, R. N. S. (1987). Data compression using dynamic markov modelling. The Computer Journal, 30(6):541-550.
  6. Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. (2001). Introduction to algorithms. MIT Press, Cambridge, MA, USA.
  7. Council, T. P. P. (2001). TPC Benchmark W, Standard Specification.
  8. Crawford, W. and Kaplan, J. (2003). J2EE Design Patterns. O'Reilly & Associates, Inc., Sebastopol, CA, USA.
  9. Deshpande, M. and Karypis, G. (2004). Selective markov models for predicting web page accesses. ACM Trans. Inter. Tech., 4(2):163-184.
  10. Ebner, E., Shao, W., and Tsai, W.-T. (2000). The fivemodule framework for internet application development. ACM Comput. Surv., 32(1es):40.
  11. Eirinaki, M., Vazirgiannis, M., and Kapogiannis, D. (2005). Web path recommendations based on page ranking and markov models. In WIDM 7805: Proceedings of the 7th annual ACM international workshop on Web information and data management, pages 2-9, New York, NY, USA. ACM Press.
  12. Galata, A., Johnson, N., and Hogg, D. (1999). Learning behaviour models of human activities. In Proc. British Mashine Vision Conference (BMVC'99), pages 12-22.
  13. Garcia, D. F. and Garcia, J. (2003). Tpc-w e-commerce benchmark evaluation. Computer, 36(2):42-48.
  14. Guo, F. and Solihin, Y. (2006). An analytical model for cache replacement policy performance. In SIGMETRICS 7806/Performance 7806: Proceedings of the joint international conference on Measurement and modeling of computer systems, pages 228-239, New York, NY, USA. ACM Press.
  15. Jordan, M., Czajkowski, G., Kouklinski, K., and Skinner, G. (2004). Extending a j2ee server with dynamic and flexible resource management. In Middleware 7804: Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware, pages 439-458, New York, NY, USA. Springer-Verlag New York, Inc.
  16. Kircher, M. and Jain, P. (2004). Pattern-Oriented Software Architecture: Patterns for Resource Management. John Wiley & Sons.
  17. Oberle, D., Eberhart, A., Staab, S., and Volz, R. (2004). Developing and managing software components in an ontology-based application server. In Middleware 7804: Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware, pages 459-477, New York, NY, USA. Springer-Verlag New York, Inc.
  18. Raman, R., Livny, M., and Solomon, M. (2003). Policy driven heterogeneous resource co-allocation with gangmatching. hpdc, 00:80.
  19. Sadaoui, S. and Sharifimehr, N. (2006). A novel object pool service for distributed systems. In The 8th International Symposium on Distributed Objects and Applications, New York, NY, USA. Springer Verlag New York, Inc.
  20. Stefanov, N., Galata, A., and Hubbold, R. (2005). Real-time hand tracking with variable-length markov models of behaviour. In CVPR 7805: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops, page 73, Washington, DC, USA. IEEE Computer Society.
  21. Sullivan, D. G. (2003). Using probabilistic reasoning to automate software tuning. PhD thesis, Harvard University Cambridge, Massachusetts. Adviser-Margo I. Seltzer.
  22. Sullivan, D. G., Seltzer, M. I., and Pfeffer, A. (2004). Using probabilistic reasoning to automate software tuning. In SIGMETRICS 7804/Performance 7804: Proceedings of the joint international conference on Measurement and modeling of computer systems, pages 404-405, New York, NY, USA. ACM Press.
  23. Vose, M. D. (1991). A linear algorithm for generating random numbers with a given distribution. IEEE Trans. Softw. Eng., 17(9):972-975.
  24. Westlund, H. B. and Meyer, G. W. (2002). A brdf database employing the beard-maxwell reflection model. In Graphics Interface, pages 189-201.
Download


Paper Citation


in Harvard Style

Sharifimehr N. and Sadaoui S. (2007). A PREDICTIVE AUTOMATIC TUNING SERVICE FOR OBJECT POOLING BASED ON DYNAMIC MARKOV MODELING . In Proceedings of the Second International Conference on Software and Data Technologies - Volume 3: ICSOFT, ISBN 978-989-8111-07-4, pages 38-45. DOI: 10.5220/0001329700380045


in Bibtex Style

@conference{icsoft07,
author={Nima Sharifimehr and Samira Sadaoui},
title={A PREDICTIVE AUTOMATIC TUNING SERVICE FOR OBJECT POOLING BASED ON DYNAMIC MARKOV MODELING},
booktitle={Proceedings of the Second International Conference on Software and Data Technologies - Volume 3: ICSOFT,},
year={2007},
pages={38-45},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001329700380045},
isbn={978-989-8111-07-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Software and Data Technologies - Volume 3: ICSOFT,
TI - A PREDICTIVE AUTOMATIC TUNING SERVICE FOR OBJECT POOLING BASED ON DYNAMIC MARKOV MODELING
SN - 978-989-8111-07-4
AU - Sharifimehr N.
AU - Sadaoui S.
PY - 2007
SP - 38
EP - 45
DO - 10.5220/0001329700380045