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QoS-aware service selection via collaborative QoS evaluation

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Abstract

We present in this paper a novel collaborative filtering based scheme for evaluating the QoS of large scale Web services. The proposed scheme automates the process of assessing the QoS of a priori unknown service providers and thus facilitates service users in selecting services that best match their QoS requirements. Most existing service selection approaches ignore the great diversity in the service environment and assume that different users receive identical QoS from the same service provider. This may lead to inappropriate selection decisions as the assumed QoS may deviate significantly from the one actually received by the users. The collaborative filtering based approach addresses this issue by taking the diversity into account instead of uniformly applying the same QoS value to different users. They predict a user’s QoS on an unknown service by exploiting the historical QoS experience of similar users. Nevertheless, when only limited historical QoS data is available, these approaches either fail to make any predictions or make very poor ones. The cornerstone of the proposed QoS evaluation scheme is a Relational Clustering based Model (or RCM) that effectively addresses the data scarcity issue as stated above. Experimental results on both real and synthetic datasets demonstrate that the proposed scheme can more accurately predict the QoS on unknown service providers. The efficient performance also makes it applicable to QoS evaluation for large scale Web services.

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  1. College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY, 14623, USA

    Qi Yu

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Correspondence to Qi Yu.

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Yu, Q. QoS-aware service selection via collaborative QoS evaluation. World Wide Web 17, 33–57 (2014). https://doi.org/10.1007/s11280-012-0186-0

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  • DOI: https://doi.org/10.1007/s11280-012-0186-0

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