A QoS-satisfied Prediction Model for Cloud-service Composition Based on Hidden Markov Model

Qingtao Wu, Mingchuan Zhang, Ruijuan Zheng, Wangyang Wei


There are various significant issues in cloud computing, such as service provision, service matching and service assessment, which have attracted researchersâ?? attentions recently. QoS play an increasingly important role during the procedure of cloud-based service provision for seamless and dynamic integration of cloud-service components. In this paper, we focus on the QoS-satisfied prediction for cloud-service composited components and present a QoS-satisfied prediction model based on hidden Markov model. For a general process of cloud-service provision, if the userâ??s QoS could not be satisfied only by one cloud-service component, the component composition should be considered to provide to user, where the QoS-satisfied capability of composited components need to be proactively predicted to guarantee the userâ??s QoS. We discuss the proposed model in detail and proof the model partly. The simulation results show that our model can obtain rather high prediction accuracy rate.

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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