Smart Root Search (SRS) in Solving Service Time–Cost Optimization in Cloud Computing Service Composition (STCOCCSC) Problems

Abstract

In this paper, the novel heuristic search algorithm called Smart Root Search (SRS) was examined for solving a set of different-sized service time–cost optimization in cloud computing service composition (STCOCCSC) problems, and its performance was compared with those of the ICACRO-C, ICACRO-I, ICA, and Niching PSO algorithms. STCOCCSC is an np-hard problem due to the large number of unique services available as well as the many service providers who provide services with different quality levels. Finding closer-to-optimal solutions supports cloud clients by providing them with higher quality-lower price services. The SRS obtained results proved that the SRS provided 6.74, 11.2, 47.95, and 87.29 percent performance improvement on average to the comparative algorithms, respectively, for all considered five problems. Furthermore, employing symmetry concepts in dividing the problem search space helps the algorithm to avoid premature convergence and any efficiency reduction while facing higher-dimensional search spaces. Due to these achievements, the SRS is a multi-purpose, flexible, and scalable heuristic search algorithm capable of being utilized in various optimization applications.