Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Introduction to Bayesian Networks
Introduction to Bayesian Networks
IEEE Internet Computing
A Dynamic Real-time Benchmark for Assessment of QoS and Resource Management Technology
RTAS '99 Proceedings of the Fifth IEEE Real-Time Technology and Applications Symposium
QoS Assessment via Stochastic Analysis
IEEE Internet Computing
Quantitative assessment of user-level QoS and its mapping
IEEE Transactions on Multimedia
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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.