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Cloud computing is emerging technology; it has Stored more amount of data. When accessing the technology, it has to face many problems like load balancing, task scheduling. The main problem is physical host in cloud data center are so overloaded. While it happens, the data center has imbalanced. In existing implementation approaches load balancing concepts. It has much complexity. For this problem, we have introduced Load balancing based Bayes theorem and clustering with some constraints.

Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. The scheduling problem is computationally hard even when there are no dependencies among jobs. Thus, the new local search (LS) and particle Swarm Optimization (PSO) algorithm seems to be efficient for the problem of batch job scheduling on computational grids. We consider the grid scheduling as a bi-objective optimization problem consisting of the minimization of the makespan and flowtime. The bi-objectivity is tackled through a hierarchic approach in which makespan is considered a primary objective and flowtime a secondary one. In this, a heuristic approach based on particle swarm optimization algorithm is adopted for solving task scheduling problem in grid environment. Particle Swarm Optimization (PSO) is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The computational results show that our PSO & TS implementation clearly outperforms the compared algorithms. This work proposes optimization technique called Tabu search that is combined with the ant colony optimization and PSO technique to solve the grid scheduling problems.