SLUM: Service Layered Utility Maximization Model to Guide Dynamic Composite Webservice Selection in Virtual Organizations

Abstract

Dynamic webservice composition is a promising ICT support service for virtual organizations. However, dynamic webservice composition remains a nondeterministic polynomial (NP) hard problem despite more than 10 years of extensive research, making the applicability of the technique to problems of industrial relevance limited. In [48], we proposed a layered method, SLUM, to combat the problem. Analytically, SLUM overcomes the relative weaknesses of two widely used approaches in the literature – the local planning (hereafter L-MIP) strategy and the Mixed Integer Programming (S-MIP) method. Despite the promising benefits of SLUM, it's unknown to what extent and under what circumstances SLUM is better or worse than L-MIP algorithms and S-MIP. The research objective of the study was to investigate the relative performance of SLUM w.r.t S-MIP and L-MIP using two performance criteria: - solution quality and CPU running time. Several randomly generated two task workflows of monotonically increasing hardness in the number of webservices per task were used to benchmark SLUM against the other two algorithms. A set of numerical and statistical techniques were used to experimentally compare the solution quality and the running time growth of SLUM against L-MIP and S-MIP. We determined that SLUM generates solutions with an average quality of 93% w.r.t the global optimum. Further, we show that SLUM yields solutions that are 5% more quality than L-MIP. On the other hand, we established that L-MIP outperforms both S-MIP and SLUM by multiple factors in terms of computational efficiency. However, we find that for problem instances with less than 22 webservices per task, S-MIP is about 1.3 times faster than SLUM. Beyond n=22, the running time of SLUM teB, expressed in terms of the running time of S-MIP teA, is given by teB= teA0.78. We also establish that SLUM is asymptotically 3.6 times faster than S-MIP on average. We conclude that in order for a virtual enterprise broker to obtain maximum benefit from dynamic service composition, the broker should combine the three techniques in the following manner- (1) for service request without global constraints requirements, L-MIP is the most suitable method to use, (2) Where there is need for global constraints and the number of service providers per task is less than 22, S-MIP is most preferred and (3) in scenarios the number of service providers per task is more than 22 and there is a need to satisfy global constraints, SLUM is superior to both S-MIP and L-MIP.