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CloudRank: A Cloud Service Ranking Method Based on Both User Feedback and Service Testing

CloudRank: A Cloud Service Ranking Method Based on Both User Feedback and Service Testing

Jianxin Li, Linlin Meng, Zekun Zhu, Xudong Li, Jinpeng Huai, Lu Liu
Copyright: © 2013 |Pages: 29
ISBN13: 9781466628540|ISBN10: 1466628545|EISBN13: 9781466628557
DOI: 10.4018/978-1-4666-2854-0.ch010
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MLA

Li, Jianxin, et al. "CloudRank: A Cloud Service Ranking Method Based on Both User Feedback and Service Testing." Principles, Methodologies, and Service-Oriented Approaches for Cloud Computing, edited by Xiaoyu Yang and Lu Liu, IGI Global, 2013, pp. 230-258. https://doi.org/10.4018/978-1-4666-2854-0.ch010

APA

Li, J., Meng, L., Zhu, Z., Li, X., Huai, J., & Liu, L. (2013). CloudRank: A Cloud Service Ranking Method Based on Both User Feedback and Service Testing. In X. Yang & L. Liu (Eds.), Principles, Methodologies, and Service-Oriented Approaches for Cloud Computing (pp. 230-258). IGI Global. https://doi.org/10.4018/978-1-4666-2854-0.ch010

Chicago

Li, Jianxin, et al. "CloudRank: A Cloud Service Ranking Method Based on Both User Feedback and Service Testing." In Principles, Methodologies, and Service-Oriented Approaches for Cloud Computing, edited by Xiaoyu Yang and Lu Liu, 230-258. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2854-0.ch010

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Abstract

In this chapter, the authors propose a Cloud service ranking system, named CloudRank, based on both the user feedback and service testing. In CloudRank, we design a new ranking-oriented collaborative filtering (CF) approach named WSRank, in which user preferences are modeled as personal rankings derived from user QoS ratings on services to address service quality predication problem. Different from the existing similar approaches, WSRank firstly presents a QoS model which allows users to express their preferences flexibly while providing combination of multiple QoS properties to give an overall rating to a service. Secondly, it measures the similarity among users based on the correlation of their rankings of services rather than the rating values. Nevertheless, it is neither accurate nor sufficient to rank Cloud services merely based on users’ feedbacks, as there are many problems such as cold-start problem, absence of user feedback, even some service faults occurred in a service workflow, so to get an accurate ranking, an active service QoS testing and fault location approach is required together with WSRank. Therefore, in CloudRank, the authors also designed an automated testing prototype named WSTester to collect real QoS information of services. WSTester integrates distributed computers to construct a virtual testing environment for Web service testing and deploys test tasks onto distributed computers efficiently.

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