Client-Centric Cloud Service Composition

Recent computing world has seen rapid growth of the number of middle and large scale enterprises that deploy business processes sharing variety of services available over cloud environment. Due to the advantage of reduced cost and increased availability, the cloud technology has been gaining unbound popularity. However, because of existence of multiple cloud service providers on one hand and varying user requirements on the other hand, the task of appropriate service composition becomes challenging. The conception of this chapter is to consider the fact that different quality parameters related to various services might bear varied importance for different user. This chapter introduces a framework for QoS-based Cloud service selection to satisfy the end user needs. A hybrid algorithm based on genetic algorithm (GA) and Tabu Search methods has been developed, and its efficacy is analysed. Finally, this chapter includes the experimental analysis to present the performance of the algorithm.

INTELLIGENT NEIGHBORHOOD EXPLORATION IN LOCAL SEARCH METHOD

Standard tabu search methods are based on the complete exploration of current solution neighborhood. However, for some problems due to the neighborhood size or to the fitness evaluation complexity, the total exploration of the neighborhood is impractical. The main purpose of this paper is to propose a local search method with no enumeration procedure. In other words, a single solution is examined at each iteration and retained for the future iterations. The idea is to randomly select one solution among a sub-set of the neighborhood of the current one. An adaptive exploration of neighborhood, using extension and restriction mechanisms represented by loop detection and tabu list structure, determines this sub-set. This approach is applied to the K-coloring problem and evaluated on standard benchmarks like DIMACS. The objective is to show how a generic method, without full neighborhood exploration, degradation control and problem-oriented operators, provides a very competitive results comparing to the best dedicated algorithms for graph coloring problems published in the literature.