DYNAMIC SEMI-MARKOVIAN WORKLOAD MODELING

Nima Sharifimehr, Samira Sadaoui

Abstract

In this paper, we present a Markovian modeling approach which is based on a combination of existing Semi-Markov and Dynamic Markov models. The proposed approach is designed to be an efficient statistical modeling tool to capture both actions intervals patterns and sequential behavioral patterns. A formal definition of this model and detailed algorithms for its implementation are illustrated. We show the applicability of our approach to model workload of enterprise application servers. However, the given formal definition of our proposed approach prepares a firm ground for academic researchers to investigate many other possible applications. Finally, we prove the accuracy of our dynamic semi-Markovian approach for the most chaotic situations.

References

  1. 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.
  2. Council, T. P. P. (2001). TPC Benchmark W, Standard Specification.
  3. Cover, T. M. and Thomas, J. A. (2006). Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience.
  4. Dhyani, D., Bhowmick, S. S., and Ng, W.-K. (2003). Modelling and predicting web page accesses using markov processes. In DEXA 7803: Proceedings of the 14th International Workshop on Database and Expert Systems Applications, page 332, Washington, DC, USA. IEEE Computer Society.
  5. 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.
  6. F. Khalil, J. L. and Wang, H. (2007). Integrating markov model with clustering for predicting web page accesses. In AusWeb'07: Proceedings of the 13th Australian World Wide Web conference, Australia.
  7. Firoiu, L. and Cohen, P. R. (2002). Segmenting time series with a hybrid neural networks - hidden markov model. In Eighteenth national conference on Artificial intelligence, pages 247-252, Menlo Park, CA, USA. American Association for Artificial Intelligence.
  8. Garcia, D. F. and Garcia, J. (2003). Tpc-w e-commerce benchmark evaluation. Computer, 36(2):42-48.
  9. López, G. G. I., Hermanns, H., and Katoen, J.-P. (2001). Beyond memoryless distributions: Model checking semi-markov chains. In PAPM-PROBMIV 7801: Proceedings of the Joint International Workshop on Process Algebra and Probabilistic Methods, Performance Modeling and Verification, pages 57-70, London, UK. Springer-Verlag.
  10. Mariucci, M. (2000). Enterprise application server development environments. Technical report, Institute of Parallel and Distributed High Performance Systems.
  11. Mosbah, M. and Saheb, N. (1997). A syntactic approach to random walks on graphs. In WG 7897: Proceedings of the 23rd International Workshop on Graph-Theoretic Concepts in Computer Science, pages 258-272, London, UK. Springer-Verlag.
  12. Ormack, G. V. and Horspool, R. N. S. (1987). Data compression using dynamic markov modelling. Comput. J., 30(6):541-550.
  13. R.J. Honicky, S. R. and Sawyer, D. (2005). Workload modeling of stateful protocols using hmms. In proceedings of the 31st annual International Conference for the Resource Management and Performance Evaluation of Enterprise Computing Systems, Orlando, Florida.
  14. Rolia, J., Cherkasova, L., and Friedrich, R. (2006). Performance engineering for enterprise software systems in next generation data centres. Technical report, HP Laboratories.
  15. Sarukkai, R. R. (2000). Link prediction and path analysis using markov chains. Comput. Networks, 33(1- 6):377-386.
  16. Sharifimehr, N. and Sadaoui, S. (2007). A predictive automatic tuning service for object pooling based on dynamic markov modeling. In Proc. of 2nd International Conference on Software and Data Technologies, Barcelona, Spain.
  17. Shaw, A. C. (2000). Real-Time Systems and Software. John Wiley & Sons, Inc., New York, NY, USA.
  18. Sicard, S., Palma, N. D., and Hagimont, D. (2006). J2ee server scalability through ejb replication. In SAC 7806: Proceedings of the 2006 ACM symposium on Applied computing, pages 778-785, New York, NY, USA. ACM.
  19. Song, B., Ernemann, C., and Yahyapour, R. (2004). Parallel computer workload modeling with markov chains. In Job Scheduling Strategies for Parallel Processing, pages 47-62, London, UK. Springer Verlag LNCS.
  20. van der Hoek, W., Jamroga, W., and Wooldridge, M. (2005). A logic for strategic reasoning. In AAMAS 7805: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, pages 157-164, New York, NY, USA. ACM Press.
  21. Vose, M. D. (1991). A linear algorithm for generating random numbers with a given distribution. IEEE Trans. Softw. Eng., 17(9):972-975.
  22. Zellner, A. (2004). Statistics, Econometrics and Forecasting. Cambridge University Press.
Download


Paper Citation


in Harvard Style

Sharifimehr N. and Sadaoui S. (2008). DYNAMIC SEMI-MARKOVIAN WORKLOAD MODELING . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8111-36-4, pages 125-130. DOI: 10.5220/0001678801250130


in Bibtex Style

@conference{iceis08,
author={Nima Sharifimehr and Samira Sadaoui},
title={DYNAMIC SEMI-MARKOVIAN WORKLOAD MODELING},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2008},
pages={125-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001678801250130},
isbn={978-989-8111-36-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - DYNAMIC SEMI-MARKOVIAN WORKLOAD MODELING
SN - 978-989-8111-36-4
AU - Sharifimehr N.
AU - Sadaoui S.
PY - 2008
SP - 125
EP - 130
DO - 10.5220/0001678801250130