A QoS-satisfied Prediction Model for Cloud-service Composition Based on Hidden Markov Model
Author(s) -
Qingtao Wu,
Mingchuan Zhang,
Ruijuan Zheng,
Wangyang Wei
Publication year - 2013
Publication title -
international journal of online and biomedical engineering (ijoe)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 8
ISSN - 2626-8493
DOI - 10.3991/ijoe.v9i3.2678
Subject(s) - quality of service , cloud computing , computer science , component (thermodynamics) , service (business) , mobile qos , matching (statistics) , distributed computing , markov process , hidden markov model , computer network , service provider , artificial intelligence , mathematics , operating system , economics , thermodynamics , physics , economy , statistics
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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