الفهرس 
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Abstract
With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more attention. Grids are playing an important and growing role in today’s networks. The Grid can fulfill in a specific time a huge amount of computations that cannot be done by using the best super computers. However, Grid performance can still be improved by making sure all the resources available in the Grid are utilized by a good load balancing algorithm. The purpose of such algorithms is to make sure all nodes are equally involved in Grid computations.
This thesis introduces a new optimization approach that is based on three distributed swarm intelligence inspired load balancing algorithms. One is based on ant colony optimization, the secand one is based on particle swarm optimization and the third is the Ant colony of pheromone control. In the Ant algorithm, an ant is invoked in response to submitting a job to the Grid and this ant surfs the network to find the best resource to deliver the job to. In the particle swarm optimization algorithm, each node plays a role as a particle and moves toward other particles by sharing its workload among them. The Ant colony of pheromone control adopts several approaches to reduce the influences from past experience and encourages the exploration of new paths or paths that were previously non-optimal.
The proposed approach is implemented using a Grid simulation toolkit (GridSim) dedicated to Grid simulations. Two experiment are made in order to evaluate the performance of the proposed approach . The results obtained show how this technology has a great effectiveness and high performance in Job Scheduling and Load Balancing for Computation Grid.