A Novel QoS Monitoring Approach Sensitive to Environmental Factors

The quality of service-oriented system relies heavily on the third-party service. Such reliance would result in many uncertainties, in consideration of the complex and changeable network environment. Hence, effective runtime monitoring technique is required by service-oriented system. Several monitoring approaches have been proposed. However, all of these approaches do not consider the influences of environmental factors such as the position of server and users, and the load at runtime. Ignoring these influences, which exist among monitoring process, may cause wrong monitoring results. In order to solve this problem, this paper proposes a novel QoS monitoring approach sensitive to environmental factors called wBSRM (weighted Bayesian Runtime Monitoring) based on weighted naive Bayesian and TF-IDF (Term Frequency-Inverse Document Frequency). The proposed approach measures influence of environmental factor by TF-IDF algorithm and then constructs weighted naïve Bayesian classifier by learning part of samples to classify monitoring results. Experiments are conducted based on both public network data set and randomly generated data set. The experimental results demonstrate that our approach is better than previous approaches.