The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Journal of Parallel and Distributed Computing
Tasks Scheduling Based on Neural Networks in Grid
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Hopfield Neural Network Approach for Task Scheduling in a Grid Environment
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
Optimization design based on improved ant colony algorithm for PID parameters of BP neural network
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Task Scheduling in computational grid is a complex optimization problem which may require consideration of different criteria such as waiting time, makespan time, throughput, communication time, and dispatching time. For optimal scheduling, the scheduler must know about the above factors and status of the resources in the grid and include these dynamic changes in the availability of resources while scheduling the tasks. For all situations, classical algorithms cannot adapt themselves with situations. The heuristic algorithms are proved to be more efficient than classical scheduling algorithms. This paper propose a method that tunes the Back Propagation Neural networks (BPN) using the capability of ACO algorithm to produce an optimal solution. Proposed method reduces the computation time by removing the unnecessary links in the neural network structure. The algorithm increases the efficiency the scheduling process and allocates the tasks to best available resources in the computation grid.