Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78503
Title: IgS-wBSRM: A time-aware Web Service QoS monitoring approach in dynamic environments
Authors: Zhang, PC
Jin, HY
He, ZP
Leung, HT 
Song, W
Jiang, Y
Keywords: Quality of Service
Time-aware
Information gain
Sliding window
Dynamic monitoring
Issue Date: 2018
Publisher: Elsevier
Source: Information and software technology, Apr. 2018, v. 96, p. 14-26 How to cite?
Journal: Information and software technology 
Abstract: Context: Quality of Service(QoS) is an important criterion to measure the quality of third-party web services. However, it is always affected by different environmental factors. Consequently, how to monitor web service QoS timely and accurately in dynamic environments is an important problem. Objective: Our article aims to design a novel Web service QoS monitoring approach which can be used in dynamic environments. Method: To achieve the above objective, we propose a novel weighted naive Bayesian runtime monitoring approach based on information gain theory and sliding window mechanism, called IgS-wBSRM. IgS-wBSRM initializes the weights of different environmental factors according to training samples collected. Then, according to information entropy and information gain theory, IgS-wBSRM reads the sample data flow in sequence, and calculates the information gain of each environmental impact factor after the arrival of new sample. It updates the initialized weights with TF-IDF algorithm in dynamic environments. In this way, IgS-wBSRM can correct the delay judgement, jitter noise and off-line constant problems in traditional monitoring approaches. Furthermore, considering the timeliness of the training samples, IgS-wBSRM combines a sliding window mechanism to update the weights-of each environmental impact factor, and it can eliminate the impact of the recent service state in the accumulated historical data. Results: A set of dedicated experiments based on a real world data set and a simulated data set demonstrates that IgS-wBSRM can abandon the expiration information of historical data effectively, and can monitor QoS more accurately. Conclusions: The overall effect of IgS-wBSRM is better than other QoS monitoring approaches. We suggest directions for follow-up work, e.g., exploring the influence of the size of the sliding window, considering multiple QoS attributes combined with data integration theory and applying IgS-wBSRM in other QoS areas.
URI: http://hdl.handle.net/10397/78503
ISSN: 0950-5849
DOI: 10.1016/j.infsof.2017.11.003
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