Synthetic Workload Generation for Cloud Computing Applications
Arshdeep Bahga, Vijay Krishna Madisetti
DOI: 10.4236/jsea.2011.47046   PDF    HTML     10,423 Downloads   20,975 Views   Citations


We present techniques for characterization, modeling and generation of workloads for cloud computing applications. Methods for capturing the workloads of cloud computing applications in two different models - benchmark application and workload models are described. We give the design and implementation of a synthetic workload generator that accepts the benchmark and workload model specifications generated by the characterization and modeling of workloads of cloud computing applications. We propose the Georgia Tech Cloud Workload Specification Language (GT-CWSL) that provides a structured way for specification of application workloads. The GT-CWSL combines the specifications of benchmark and workload models to create workload specifications that are used by a synthetic workload generator to generate synthetic workloads for performance evaluation of cloud computing applications.

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A. Bahga and V. Madisetti, "Synthetic Workload Generation for Cloud Computing Applications," Journal of Software Engineering and Applications, Vol. 4 No. 7, 2011, pp. 396-410. doi: 10.4236/jsea.2011.47046.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] G. Abdulla, “Analysis and Modeling of World Wide Web Traffic,” Ph.D. Thesis, Virginia Polytechnic Institute and State University, Blacksburg, 1998.
[2] M. Crovella and A. Bestavros, “Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes,” IEEE/ACM Transactions on Networking, Vol. 5, No. 6, 1997, pp. 835-846. doi:10.1109/90.650143
[3] D. Mosberger and T. Jin, “Httperf: A Tool for Measuring Web Server Performance,” ACM Performance Evaluation Review, Vol. 26, No. 3, 1998, pp. 31-37. doi:10.1145/306225.306235
[4] D. Garcia and J. Garcia, “TPC-W E-Commerce Benchmark Evaluation,” IEEE Computer, Vol. 36, No. 2, 2003, pp. 42-48.
[5] RUBiS, 2010.
[6] SPECweb99, 2010.
[7] TPC-W, 2010.
[8] WebBench, 2010.
[9] P. Barford and M. E. Crovella, “Generating Representative Web Workloads for Network and Server Performance Evaluation,” Proceedings of the 1998 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, Madison, 22-26 June 1998, pp. 151-160.
[10] D. Krishnamurthy, J. Rolia and S. Majumdar, “A Synthetic Workload Generation Technique for Stress Testing Session-Based Systems,” IEEE Transactions on Software Engineering, Vol. 32, No. 11, 2006, pp. 868-882.
[11] A. Mahanti, C. Williamson and D. Eager, “Traffic Analysis of a Web Proxy Caching Hierarchy,” IEEE Network, Vol. 14, No. 3, 2000, pp. 16-23. doi:10.1109/65.844496
[12] S. Manley, M. Seltzer and M. Courage, “A Self-Scaling and Self-Configuring Benchmark for Web Servers,” Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, Madison, June 1998, pp. 270-271.
[13] Webjamma, 2010.
[14] K. Kant, V. Tewari and R. Iyer, “Geist: A Generator for E-Commerce & Internet Server Traffic,” IEEE International Symposium on Performance Analysis of Systems and Software, Tucson, 4-5 November 2001, pp. 49-56.
[15] E. Vidal, F. Thollard, C. Higuera, F. Casacuberta and R. C. Carrasco, “Probabilistic Finite-State Machines Part I,” IEEE Transactions of Pattern Analysis and Machine Intelligence, Vol. 27, No. 7, 2005, pp. 1013-1025.
[16] MLE Tool, 2010.
[17] Kolmogorov-Smirnov Test, 2010.
[18] Faban, 2010.

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