Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66279
Title: A novel QoS prediction approach for cloud service based on Bayesian networks model
Authors: Zhang, P
Han, Q
Li, W
Leung, H
Song, W
Keywords: Bayesian network model
Cloud computing
QoS Prediction of Cloud Services
Quality of Service
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - 2016 IEEE International Conference on Mobile Services, MS 2016, 2016, 7787062, p. 111-118 How to cite?
Abstract: Considered as the next generation computing model, cloud computing plays an important role in scientific and com-mercial computing and draws wide attention from both academiaand industry. In the dynamic, complex and changeable cloudcomputing environment, Quality Of Service (QoS) is an impor-tant basis for the selection of different cloud services. Therefore, the prediction of cloud services QoS can help users to choose themost suitable service at hand. The software and hardware andresources of three-layer structure for cloud computing will impacton cloud services QoS, but existing QoS prediction approachesare not consider the three-layer structure on the influence of thecloud service QoS. The CPU usage, physical memory usage andthe number of processes of infrastructure layer have definitelyinfluenced QoS. In order to address this limitation, in the paper, a Bayesian network model of QoS prediction for cloud servicesis proposed. Firstly, an initial and basic Bayesian network modelis established by collecting data from the infrastructure layer, the platform layer and the application layer. Then the Bayesiannetwork is trained and updated to obtain the cloud service QoSprediction model. Finally, a set of experiments based on collecteddata from the real cloud service environment has been conductedto validate the proposed approach. Experimental results showthat the prediction approach is effective and accurate.
Description: 2016 IEEE 5th International Conference on Mobile Services, MS 2016, San Francisco, US, 27 June - 2 July 2016
URI: http://hdl.handle.net/10397/66279
ISBN: 9781509026258
DOI: 10.1109/MobServ.2016.26
Appears in Collections:Conference Paper

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