Toward SLA-constrained service composition: An approach based on a fuzzy linguistic preference model and an evolutionary algorithm

In a market-oriented service computing environment, both back-end SLA (service level agreement) offers and front-end SLA requirements should be considered when performing service composition. In this paper, we address the optimization problem of SLA-constrained service composition and focus on the following issues: the difficulties related to preference definition and to weight assignment, the limitation of linear utility functions in identifying preferred skyline solutions, and the efficiency and scalability requirements of the optimization algorithm. We present a systematic approach based on a fuzzy preference model and on evolutionary algorithms. Specifically, we first model this multi-objective optimization problem using the weighted Tchebycheff distance rather than a linear utility function. We then present a fuzzy preference model for preference representation and weight assignment. In the model, a set of fuzzy linguistic preference terms and their properties are introduced for establishing consistent preference order of multiple QoS dimensions, and a weighting procedure is proposed to transform the preference into numeric weights. Finally, we present two evolutionary algorithms, i.e., single_EA and hybrid_EA, that implement different optimization objectives and that can be used in different SLA management scenarios for service composition. We conduct a set of experimental studies to evaluate the effectiveness of the proposed algorithms in determining the optimal solutions, and to evaluate their efficiency and scalability for different problem scales.