Grid Scheduling using PSO with Naive Crossover

Grid computing can be defined as applying the resources of many computers in a network to a problem which requires a great number of computer processing cycles or access to large amounts of data. Thetask scheduling problem is the problem of assigning the tasks in the system in a manner that will optimize the overall performance of the application, while assuring the correctness of the result. In this paper we use the technique of PSO with Naive crossover to solve the taskscheduling problem in grid computing. The aim of using thistechnique is use the given resources optimally and assign the task to the resources efficiently. The simulated results show that PSO with Naive Crossover proves to be a better algorithm when applied to resource allocation anddisk scheduling in grid computing.

[1]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[2]  Yuehui Chen,et al.  A Task Scheduling Algorithm Based on PSO for Grid Computing , 2008 .

[3]  Yang Gao,et al.  Adaptive grid job scheduling with genetic algorithms , 2005, Future Gener. Comput. Syst..

[4]  Rajkumar Buyya,et al.  Nature's heuristics for scheduling jobs on Computational Grids , 2000 .

[5]  Alioune Ngom,et al.  Genetic algorithm based scheduler for computational grids , 2005, 19th International Symposium on High Performance Computing Systems and Applications (HPCS'05).

[6]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[7]  Anthony A. Maciejewski,et al.  Task Matching and Scheduling in Heterogenous Computing Environments Using a Genetic-Algorithm-Based Approach , 1997, J. Parallel Distributed Comput..

[8]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[9]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[10]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[11]  Shanshan Song,et al.  Security-driven heuristics and a fast genetic algorithm for trusted grid job scheduling , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[12]  Lin Jian.  Scheduling in Grid Computing Environment Based on Genetic Algorithm , 2004 .

[13]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[14]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).