Comparison of Service Selection Algorithms for Grid Services: Multiple Objective Particle Swarm Optimization and Constraint Satisfaction Based Service Selection

Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Furthermore, Grid computing has also leveraged Web services to define standard interfaces for grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are optimally assigned to the high volume of incoming requests, it is important to have an efficient service selection algorithm. The algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current quality of service standards. This paper, proposes and compares two service selection algorithms on the Grid: the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique (MOPSO-CD) to the Constraint Satisfaction based Matchmaking (CS-MM) algorithm.