A Multiple System Performance Monitoring Model for Web Services
With the exponential growth of the world wide web, web services have becoming more and more popular. However, performance monitoring is a key issue in the booming service-orient architecture regime. Under such loosely coupled and open distributed computing environments, it is necessary to provide a performance monitoring model to estimate the likely performance of a service provider. Although much has been done to develop models and techniques to assess performance of services (i.e. QOSs), most of solutions are based on deterministic performance monitoring value or boolean logic. Intuitively, probabilistic representation could be a more precise and nature way for performance monitoring. In this paper, we propose a Bayesian approach to analyze service provider’s behavior to infer the rationale for performance monitoring in the web service environment. This inference facilitates the user to predict service provider’s performance, based on historical temporal records in a sliding window. Distinctively, it combines evidences from another system (For example, recommendation opinions of third parties) to provide complementary support for decision making. To our best of knowledge, this is the first approach to squeeze a final integrated performance prediction with multiple systems in Web services.
KeywordsBayesian Network Performance Monitoring Bayesian Network Model Conditional Probability Table Target Service
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- 1.Menascé, D.: QoS Issues in Web Services. IEEE Internet Computing, 72–75 (2002)Google Scholar
- 2.Dasgupta, P.: Trust as a commodity. Trust: Making and Breaking Cooperative Relations, 49–72 (1988)Google Scholar
- 5.Hilden, J., Habbema, J., Bjerregaard, B.: The measurement of performance in probabilistic diagnosis. II. Trustworthiness of the exact values of the diagnostic probabilities. Methods Inf. Med. 17(4), 227–237 (1978)Google Scholar
- 6.Despotovic, Z., Aberer, K.: A Probabilistic Approach to Predict Peers’ Performance in P2P Networks. LNCS, pp. 62–76. Springer, Heidelberg (2004)Google Scholar
- 11.Welch, L., Shirazi, B.: A Dynamic Real-Time Benchmark for Assessment of QoS and Resource Management Technology. In: The IEEE Real-Time Technology and Applications Symposium, pp. 36–45 (1999)Google Scholar
- 13.Bernardi, S., Merseguer, J.: QoS Assessment via Stochastic Analysis. IEEE Internet Computing, 32–42 (2006)Google Scholar
- 14.Easy Share, http://www.easy-share.com/
- 15.Megaupload, http://www.megaupload.com/
- 17.La Porta, R., De Silanes, F., Shleifer, A., Vishny, R.: Trust in Large Organizations. In: Nber Working Paper (1996)Google Scholar
- 20.Jensen, F.: An introduction to Bayesian networks. UCL Press, London (1996)Google Scholar