International Journal on Information Theory (IJIT), Vol.4, No.3, July 2015 DOI: 10.5121/ijit.2015.4302 1 USE OF GENETIC ALGORITHM FOR BALANCING THE GRID LOAD Saad Masood Butt 1 , Moaz Masood Butt 2 and Azura Onn 3 12 Computer and Software Engineering Department, Bahria University Islamabad, Pakistan saadmasoodbutt668@yahoo.com moazbutt786@hotmail.com 3 Department of Management and Human Resource, Universiti Tenaga National, Malaysia azura@uniten.edu.my ABSTRACT 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. 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. 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 algorithm 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. KEYWORDS Network Protocols, Grid Computing, Load Balancing, Distributed Computing, Bubble Sort, 2-element Insertion Sort 1. INTRODUCTION With the advent of distributed computing we encounter large number issues regarding processing powers and load balancing across the distributed networks specially like grid computing. In order to get an optimized results without any time delay and with maximum throughput we need enhanced algorithms across the networks. Hence this faster processing results can be obtained by introducing a single processing unit or it can be inflated to diversity of processing types like multiple processing nodes, geographically distributed processing units and parallel processing. Grid computing is a modern and diverse form of distributed computing. It focused on the ability to support computation across multiple administrative domains that sets it apart from traditional distributed computing. Grids presents a method of using the computational resources optimally within an association involving different computing resources. Its supports multiple administrative domains and security authentication and other organizational mechanisms that capable it to be distributed locally or dispersed on the multiple geographic location in form of wide area network. Within the network connected nodes can share their resources over a network domain. These resources can be utilized by any other node that is connects to the grid network. I we ponder over this fact then we consider resource sharing across the grid computing network is an important factor over the internet networks (Neil Y, 2012). Multiple sharing resources are accumulated across the grid network. These resources involves number of dimensions including computational powers, processing capabilities. In grid