M. Al Ghamdi, A. P. Chester, L. He, S. A. Jarvis


This work is concerned with dynamic resource allocation for multi-tiered, cluster-based web hosting environments. Dynamic resource allocation is reactive, that is, when overloading occurs in one resource pool, servers are moved from another (quieter) pool to meet this demand. Switching servers comes with some overhead, so it is important to weigh up the costs of the switch against possible system gains. In this paper we combine the reactive behaviour of two well known switching policies – the Proportional Switching Policy (PSP) and the Bottleneck Aware Switching Policy (BSP) – with the proactive properties of several workload forecasting models. Seven forecasting models are used, including Last Observation, Simple Algorithm, Sample Moving Average, Exponential Moving Algorithm, Low Pass Filter and Autoregressive Moving Average. As each of the forecasting schemes has its own bias, we also develop three meta-forecasting algorithms (the Active Window Model, the Voting Model and the Selective Model) to ensure consistent and improved results. We show that request servicing capability can be improved by as much as 40% when the right combination of dynamic server switching and workload forecasting are used. As important is that we can generate consistently improved results, even when we apply this scheme to real-world, highly-variable workload traces from several sources.


  1. Al-Ghamdi, M., Chester, A. P., and Jarvis, S. A. (2010). Predictive and dynamic resource allocation for enterprise applications. In Proceedings of the 2010 10th IEEE International Conference on Scalable Computing and Communications (ScalCom), pages 2776- 2783, Washington, DC, USA. IEEE Computer Society.
  2. Arlitt, M. and Williamson, C. (1996). Web server workload characterization: the search for invariants. SIGMETRICS Perform. Eval. Rev., 24(1):126-137.
  3. Casale, G. and Serazzi, G. (2004). Bottlenecks identification in multiclass queueing networks using convex polytopes. In 12th Annual Meeting of the IEEE Int'l Symposium on Modelling, Analysis, and Simulation of Comp. and Telecommunication Systems (MASCOTS).
  4. Cavendish, D., Koide, H., Oie, Y., and Gerla, M. (2010). A mean value analysis approach to transaction performance evaluation of multi-server systems. Concurr. Comput. : Pract. Exper., 22(10):1267-1285.
  5. Cherkasova, L. and Phaal, P. (2002). Session-based admission control: A mechanism for peak load management of commercial web sites. IEEE Trans. Comput., 51(6):669-685.
  6. Chester, A. P., Xue, J. W. J., He, L., and Jarvis, S. A. (2008). A system for dynamic server allocation in application server clusters. In ISPA 7808: Proceedings of the 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications, pages 130- 139, Washington, DC, USA. IEEE Computer Society.
  7. Cuomo, G. (2000). IBM WebSphere Application Server Standard and Advanced Editions; A methodology for performance tuning. IBM.
  8. Dushay, N., French, J. C., and Lagoze, C. (September 1999). Predicting indexer performance in a distributed digital library. In Third European Conference on Research and Advanced Technology for Digital Libraries (ECDL99), Paris, France.
  9. Faraz, A. and Vijaykumar, T. (2010). Joint optimization of idle and cooling power in data centers while maintaining response time. SIGPLAN Not., 45(3):243-256.
  10. Federgruen, A. and Groenevelt, H. (1986). The greedy procedure for resource allocation problems: Necessary and sufficient conditions for optimality. Oper. Res., 34(6):909-918.
  11. Gilly, K., Alcaraz, S., Juiz, C., and Puigjaner, R. (2004). Comparison of predictive techniques in cluster-based network servers with resource allocation. Modeling, Analysis, and Simulation of Computer Systems, International Symposium on, pages 545-552.
  12. Hsieh, C. and Lam, S. (1988). Pam-a noniterative approximate solution method for closed multichain queueing networks. SIGMETRICS Perform. Eval. Rev., 16(1):261-269.
  13. Keung, H. N. L. C., Dyson, J. R. D., Jarvis, S. A., and Nudd, G. R. (2003). Predicting the performance of globus monitoring and discovery service (mds-2) queries. In Proceedings of the 4th International Workshop on LBNL (2008). Internet Traffic Archive Hosted at Lawrence Berkeley National Laboratory. http://ita.ee.lbl.gov/html/traces.html.
  14. Litoiu, M. (2007). A performance analysis method for autonomic computing systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2(1):3.
  15. Little, J. ((May - Jun., 1961)). A proof for the queuing formula: L= l w. Operations Research, 9(3):383-387.
  16. Liu, Z., Squillante, M., and Wolf, J. (2001). On maximizing service-level-agreement profits. In EC 7801: Proceedings of the 3rd ACM conference on Electronic Commerce, pages 213-223, New York, NY, USA. ACM.
  17. Mahanti, A., Williamson, C., and Wu, L. (2009). Workload characterization of a large systems conference web server. In Proceedings of the 2009 Seventh Annual Communication Networks and Services Research Conference, pages 55-64, Washington, DC, USA. IEEE Computer Society.
  18. Marzolla, M. and Mirandola, R. (2007). Performance prediction of web service workflows. The third International Conference on the Quality of SoftwareArchitectures (QoAS), pages 127-144.
  19. Menascé, D. (2003). Workload characterization. In IEEE Internet Computing, pages 89-92, Piscataway, NJ, USA. IEEE Educational Activities Department.
  20. Menascé, D. and Almeida, V. (May 7, 2000). Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning. Prentice Hall, Upper Saddle River, NJ.
  21. Menascé, D. and Almeida, V. (September 21, 2001). Capacity Planning for Web Services: Metrics, Models, and Methods. Prentice Hall, Upper Saddle River, NJ.
  22. Reiser, M. and Lavenberg, S. (1980). Mean-value analysis of closed multichain queuing networks. Journal of the Association for Computing Machinary, 27(2):313- 322.
  23. Rolia, J., Zhu, X., Arlitt, M., and Andrzejak, A. (2004). Statistical service assurances for applications in utility grid environments. Perform. Eval., 58(2+3):319-339.
  24. Tantawi, A., Towsley, G., and Wolf, J. (1988). Optimal allocation of multiple class resources in computer systems. SIGMETRICS Perform. Eval. Rev., 16(1):253- 260.
  25. Urgaonkar, B., Shenoy, P., Chandra, A., and Goyal, P. (2005). Dynamic provisioning of multi-tier internet applications. In ICAC 7805: Proceedings of the Second International Conference on Automatic Computing, pages 217-228, Washington, DC, USA. IEEE Computer Society.
  26. Xue, J. W. J., Chester, A. P., He, L., and Jarvis, S. A. (2008). Dynamic resource allocation in enterprise systems. In ICPADS 7808: Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems, pages 203-212, Washington, DC, USA. IEEE Computer Society.
  27. Yang, C. and Luo, M. (2000). Realizing fault resilience in web-server cluster. In Proceedings of the 2000 ACM/IEEE conference on Supercomputing, Supercomputing 7800, Washington, DC, USA. IEEE Computer Society.
  28. Zalewski, A. and Ratkowski, A. (2006). Evaluation of dependability of multi-tier internet business applications with queueing networks. In Proceedings of the International Conference on Dependability of Computer Systems, pages 215-222, Washington, DC, USA. IEEE Computer Society.

Paper Citation

in Harvard Style

Al Ghamdi M., P. Chester A., He L. and A. Jarvis S. (2011). DYNAMIC RESOURCE ALLOCATION AND ACTIVE PREDICTIVE MODELS FOR ENTERPRISE APPLICATIONS . In Proceedings of the 1st International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-8425-52-2, pages 551-562. DOI: 10.5220/0003388705510562

in Bibtex Style

author={M. Al Ghamdi and A. P. Chester and L. He and S. A. Jarvis},
booktitle={Proceedings of the 1st International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},

in EndNote Style

JO - Proceedings of the 1st International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
SN - 978-989-8425-52-2
AU - Al Ghamdi M.
AU - P. Chester A.
AU - He L.
AU - A. Jarvis S.
PY - 2011
SP - 551
EP - 562
DO - 10.5220/0003388705510562