Hybridization of Genetic Algorithm with Cuckoo Search Algorithm for Job Scheduling on Distributed Heterogeneous Computing Systems

Scheduling problems are at the heart of any Grid Computing system. Grid computing enables sharing, selection, aggregation and collections of geographically distributed heterogeneous resources to increase computational, and storage power and resource scheduling for solving NP-Hard problems. Different types of scheduling based on criteria, such as static, dynamic environment, adaptivity etc. The research already done in the field of other scheduling problems can be modified to be applied in this new scenario. Thus, we present an extensive study on the evolutionary hybridization of meta-heuristics methods for designing efficient Grid schedulers when makespan and flowtime are minimized. In this paper we present a hybridization of Genetic Algorithms (GA) and Cuckoo Search (CS) for scheduling in computational grids. We evaluated the proposed hybrid algorithm using different Grid scenarios generated by a Grid simulator. This proposed technique is effectively schedule jobs onto available resources and result showed that our scheduler outperform existing implementation in a grid environment thus resulting and also revealed their efficiency when makespan and flowtime are minimized.