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 t eB , expressed in terms of the running time of S-MIP t eA , is given by t eB = t eA 0.78. 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.

A major benefit of service composition is the ability to support agile global collaborative virtual organizations. However, being global in nature, collaborative virtual organizations can have several virtual industry clusters (VIC), where each VIC has hundreds to thousands of virtual enterprises that provide functionally similar services exposed as web services. These web services can be differentiated on a high dimensionality of quality of service attributes. The dilemma the virtual enterprise broker is faced with is how to dynamically select the best combination of component services to fulfill a complex consumer need within the shortest time possible. This composite service selection problem remains a Multi-Criteria Decision Making (MCDM) NP hard problem. Although existing MCDM methods based on local planning are linearly scalable for large problems, they lack capabilities to express critical intertask constraints that are practically relevant to service consumers. MCDM global planning methods on the other hand suffer exponential state space explosion making them severely limited for large problems of industrial relevance. This paper proposes HMSCM: Hierarchical Multi-Layer Service Composition Model. HMSCM is based on the theory of Layering as Optimization Decomposition [28][29][30][31]. We view the service selection process as a "two layer network" where each layer is a subproblem to be solved. The objective of one of the layers is to maximize a local utility function over a subset of web service QoS attributes from a service consumer perspective. The objective of the other layer is to maximize a local utility function over another subset of web service QoS attributes from the perspective of the Virtual enterprise broker. We develop the algorithm: Service Layered Utility Maximization (SLUM) that extends the Mixed Integer programming model in [9]. We then formulate the problem at each layer in form of SLUM. Together, the two layers attempt to achieve the global optimization objective of the network. We show analytically how HMSCM overcomes the shortcomings of existing local planning and global planning service selection methods while retaining the strengths from each. i.e HMSCM is able to scale linearly with increasing number of QoS variables and number of web services while being able to enforce global intertask constraints.

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