![]() Grid Computing resources shall perform realtime load forecasting where the results will be returned to each power system for decentralized load balancing operations. An interface between the power systems and the Grid Computing is developed that interacts with other power systems connected to the Grid Computing. Grid Computing is a gateway to virtual storage media and processing power, this paper describes how grid computing can be utilized to perform load balancing for these distributed power systems. This need requires geographically-distributed power systems to be integrated as a single entity where among the main features of this integration are large data base and computing intensive. The need for renewable power generation in power systems is becoming more importance. The experimental results demonstrate that proposed strategy effectively schedule the grid jobs in fault tolerant way in spite of highly dynamic nature of grid Through simulation we have evaluated the performance of the proposed strategy. ![]() Further, it increases the percentage of jobs executed within specified deadline and allotted budget, hence helping in making grid trustworthy. Using check pointing proposed scheme can make grid scheduling more reliable and efficient. Whenever a resource broker has job to schedule it uses the resource fault occurrence history information from GIS and depending on this information use different intensity of check pointing and replication while scheduling the job on resources which have different tendency towards fault. ![]() Proposed strategy maintains history of the fault occurrence of resource in grid information service (GIS). We devise a strategy for fault tolerant job scheduling in computational grid. In this paper, we address the problem of fault tolerance in term of resource failure. Therefore, fault tolerance has become a crucial area in grid computing. In large-scale grids, the probability of a failure is much greater than in traditional parallel systems. Due to harmonizing the resources' characteristics and tasks, the proposed algorithm is able to reduce the response time of the submitted tasks while it is simple to be implemented. To avoid stagnation, a comparison between a predefined threshold and the pheromone value of each resource is performed to keep the number of assigned tasks below this threshold. After this assigned task is executed properly, a global pheromone update is performed to renew the status of all resources for the next submitted tasks. By choosing the best resource for the submitted task, a local pheromone update is applied to the selected one to reduce the tendency of being selected by onward new tasks. In the proposed algorithm, the resource manager of the system finds the best resource for a submitted task according to a matrix that indicates the characteristics of all resources as pheromone values. To prevent this scenario, a load balancing algorithm based on Ant Colony algorithm and Max-min technique is proposed in this paper. Stagnation occurs when a large number of submitted tasks are assigned to a specific resource and make it overflow. Stagnation is one of the complicated issues in Grid computing systems, which is caused by random arrival of tasks and heterogeneous resources. Conclusion of the comparative study states that overall average tasks waiting time is enhanced by approximately 30% by using the X-levels/XD-binary tree approach against 4-levels/RMFF approach After that, experiments and provided results give a practical evaluation of these approaches from different perspectives. Then the description of currently existing approaches will be presented. First of all, introduction to grid computing and job scheduling techniques is provided. The involved approaches in this paper are “4-levels/RMFF” and our previously published approach “X-Levels/XD-Binary Tree”. ![]() There are many approaches in job scheduling in grid computing.This paper provides an experimental study of different approaches in grid computing job scheduling. Job scheduling is the activity to schedule the submitted jobs in the grid environment. One of the main challenges in grid computing environment is the way of handling the jobs(tasks) in the grid environment. The dramatic changes in the complexity of scientific applications and part of non-scientific applications increase the need for distributed systems in general and grid computing specifically. Grid computing is one of the most interesting research areas for present and future computing strategy and methodology.
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