Analytic Profit Optimization of Service-Based Systems

Service computing has become a dominant paradigm enabling the building of complex service-oriented systems, with the aim of business added-value. Because these systems are inevitably based on uncontrollable services on the unpredictable Internet, it is important to find effective ways of maximizing the profit of service-oriented systems in such unreliable environment. In this paper, we propose an analytic approach that employs a build-time analysis of the runtime dynamics of service execution to maximize the net profit from delivering composite services under full probability of uncertainty. We also present methods for improving the optimization efficiency, including reusing intermediate computation results and adopting specialized profit optimization algorithms. The superiority of the proposed approach is both theoretically proved and empirically demonstrated through experiments.