Abstract

Distributed computing is a developing with worldview with extensive heterogeneous independent frameworks with adaptable computational capacity. For this planning is an essential to enhance the general calculation and increase the benefit. Cloud computing is the form of distributed computing and also a variant of grid computing. It uses highly in commercial and research purpose but one basic challenge is scheduling of the computation process. Scheduling of computation process is NP-hard problem. So effective task scheduler has adaptive sense to reduce the computation time and increase the utilization by increasing throughput. In this paper experiment performed on different optimization algorithms like BFO, ACO, and Genetic algorithm. BFO perform significant effective in throughput, energy, response time and execution time. The average improvement is 10-20% in every defined parameter.

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