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To improve the global throughput of grid environments, workloads have to be evenly balanced among the available resources. However for computational grid we must address main new issues. Like heterogeneous autonomy, dynamicity and so forth. A framework consisting of distributed dynamic load balancing algorithm in perspective to minimize the average response time of tasks. The main goal is to prevent, if possible, the condition where some processors are overloaded with a set of tasks while others are lightly loaded or even idle. This is due to the characteristics of Grid computing and the complex nature of the problem itself. Load balancing and job migration allocate processes to interconnected workstations on a network to better take advantage of available resources. Job migration in a distributed computer system can be performed for performance enhancement and we call this activityload balancing with job migration. The intractability of this load balancing model suggests obtaining approximate solutions. In this paper, we Cognitive analysis of different approaches of load balancing and job migration into distributed system. By using a good technique approximate solutions can be obtained. Our contribution in this paper is to find out the loophole in all the previous paper related in load balancing and job migration and work upon on those gaps in my research work and it is beneficial for all those researches who want to make more enhance and efficient load balancing techniques in near future.