Finding Preferred Skyline Solutions for SLA-Constrained Service Composition

In this paper, we address the optimization problem of SLA-constrained service composition. Focusing on the main drawbacks of traditional approaches surveyed:1) the difficulties in preference definition and weight assignment, 2) the limitation of linear utility function for identifying preferred skyline solutions, and 3) the poor efficiency and scalability of algorithms, we present a systematic approach of combining the weighted Tchebycheff distance with skyline computation to cope with this optimization problem. More specifically, we first propose a fuzzy linguistic preference model that can help service composer elicit, represent and establish consistent preference relations upon QoS dimensions. Then we present a weighting procedure to transform the preference relations into numeric weights that are used in the Tchebycheff distance as quantified measurement of preference for skyline solutions. Finally we propose a hybrid evolutionary algorithm to heuristically find preferred skyline solutions in an efficient way. The algorithm is further evaluated by a set of experimental studies.