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efficiency and utility of cloud computing based on scheduling and balancing of load over cloud computing. The load balancing is important factor regarding the performance of cloud computing. Now a day's various heuristic function are used for the balancing and scheduling of load in cloud computing. Some heuristic function faced a problem of size of data and discontinuity of sequence of data. In this paper used particle of swarm optimization technique for the balancing of job in cloud environment. The nature of dynamicity of particle of swarm optimization supports the concept of dynamic load balancing technique. The modified load balancing algorithm simulate cloudsim simulator and used two other algorithm such as round robin and genetic algorithm. For the evaluation of performance cerate multiple size of job load matrix. Our experimental result shows that better performance instead of round robin and genetic algorithm. KeywordsComputing, Load balancing, swarm intelligence, PSO.

Improved the Response Throughput of Load Balancing of Scientific Cloud using Particle of Swarm Optimization