Issue No. 01 - January-March (2010 vol. 3)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TSC.2010.7
Qi Yu , Rochester Institute of Technology, Rochester
Athman Bouguettaya , CSIRO, ICT Center, Acton ACT, Australia
The performance of a service provider may fluctuate due to the dynamic service environment. Thus, the quality of service actually delivered by a service provider is inherently uncertain. Existing service optimization approaches usually assume that the quality of service does not change over time. Moreover, most of these approaches rely on computing a predefined objective function. When multiple quality criteria are considered, users are required to express their preference over different (and sometimes conflicting) quality attributes as numeric weights. This is rather a demanding task and an imprecise specification of the weights could miss user-desired services. We present a novel concept, called p-dominant service skyline. A provider S belongs to the p-dominant skyline if the chance that S is dominated by any other provider is less than p. Computing the p-dominant skyline provides an integrated solution to tackle the above two issues simultaneously. We present a p-R-tree indexing structure and a dual-pruning scheme to efficiently compute the p-dominant skyline. We assess the efficiency of the proposed algorithm with an analytical study and extensive experiments.
Quality of service, service optimization, service selection, skyline analysis, uncertainty.
Q. Yu and A. Bouguettaya, "Computing Service Skyline from Uncertain QoWS," in IEEE Transactions on Services Computing, vol. 3, no. , pp. 16-29, 2010.