Abstract—Grid computing is a form of distributed computing but different from conventional distributed computing in a manner that it tends to be heterogeneous, more loosely coupled and dispersed geographically. Grid computing can involve lot of computational tasks which requires trustworthy computational nodes. Load balancing in grid computing is a technique which overall optimizes the whole process of assigning computational tasks to processing nodes. Optimization of this process must contains the overall maximization of resources utilization with balance load on each processing unit and also by decreasing the overall time or output. Evolutionary algorithms like genetic algorithms have studied so far for the implementation of load balancing across the grid networks. But problem with these genetic algorithms is that they are quite slow in cases where large number of tasks needs to be processed. In this paper we give a novel approach of parallel genetic algorithms for enhancing the overall performance and optimization of managing the whole process of load balancing across the grid nodes

 

Index Terms—distributed computing, grid computing, parallel genetic algorithm, fitness function.

Cite: Nadra Tabassam Inam, Daud Awan, and Hameed Ur Rehman, "Grid Load Balancing Using Parallel Genetic Algorithm," International Journal of Electronics and Electrical Engineering, Vol. 3, No. 6, pp. 451-456, December 2015. doi: 10.12720/ijeee.3.6.451-456