Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Wang, Xiaogang, Cao, Jian and Xiang, Yang 2015, Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing, Journal of systems and software, vol. 100, pp. 195-210, doi: 10.1016/j.jss.2014.10.047.

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Title Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing
Author(s) Wang, Xiaogang
Cao, Jian
Xiang, Yang
Journal name Journal of systems and software
Volume number 100
Start page 195
End page 210
Total pages 16
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-02
ISSN 0164-1212
Keyword(s) Adaptive learning mechanism
Cloud service broker
Dynamic cloud service selection
Science & Technology
Technology
Computer Science, Software Engineering
Computer Science, Theory & Methods
Computer Science
WEB SERVICES
NEGOTIATION
WORKFLOW
Summary Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.
Language eng
DOI 10.1016/j.jss.2014.10.047
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072042

Document type: Journal Article
Collection: School of Information Technology
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