# Performance Evaluation of Task Scheduling in Cloud Environment Using Soft Computing Algorithms

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3 389 algorithm like GA. The movement of each particle is coordinated by a velocity which has both magnitude and direction. Particles position is influenced by its best position (local best) and position of the best particle (global best) in the problem space. The performance of the particle is measured by a problem specific fitness value. PSO algorithm has 3 steps, repeated till a stopping condition is met [17]: 1. Evaluate the fitness of each particle 2.Update individual and global best fitness and positions 3. Update velocity and position of each particle The average time required for each and every task on all the resources is computed. It is generally observed that the time reduces as the cost of communication increases. All the tasks are mapped in the workflow. PSO finds global minima quickly and also attain balanced distribution of workload onto resources. Fitness evaluation is conducted by supplying a candidate solution to an objective function. Individual and global best fitness and positions are updated by comparing newly evaluated fitness against earlier individual and global best fitness, and replacing best fitness and positions as necessary. Velocity and position update step is responsible for PSO algorithm s optimization ability. PSO algorithm is summarized as follows [18]. 1. Initialize the swarm X i, the position of particles are randomly initialized within the feasible space. 2. Evaluate the performance F of each particle, using its current position X i (t). 3. Compare the performance of each individual to its best performance so far: if F(X i (t))<f(p ibest ): F(P ibest )=F(X i (t)) P ibest = X i (t) 4. Compare the performance of each particle to the global best particle: iff (X i (t))<f (P gbest ): F (P gbest )=F (X i (t)) P gbest = X i (t) 5. Change the velocity of the particle. 6. Move each particle to a new position. 7. Go to step 2, and repeat until convergence. Table.1 Parameters Used Parameters Values Resources Used 4 Jobs 40,80,160,320,640 CPU used 1 No of Tasks Makespan Table.2 Makespan (In seconds) MinMin MaxMin PSO BCO Number of tasks Fig.1 Task Vs Makespan (In Seconds) Table.3 Resource Utilization Min-Min Max-Min PSO BCO No of tasks Min-Min Max-Min BCO PSO RAM Size Task Size 1 GB 1-7 Units

5 391 International Conference on Communications and Information Technology, 2012, pp [13] Mizan, T., Masud, S.M.R.A., Latip, R., Modified Bees Life Algorithm for Job Scheduling in Hybrid Cloud, International Journal of Engineering and Technology Volume 2 No. 6, June, 2012, [14] Pandey, S., Wu, L., Guru, S.M., and Buyya, R, "A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments", In Proceedings of 24th IEEE International Conference on Advanced Information Networking and Applications,2010, pp [15] Shaobin Zhan and Hongying HUo, Improved PSO based Task Scheduling Algorithm in Cloud Computing, Journal of Information and Computational Science, 2012 November, pp [16] Salim Bitam, "Bees Life Algorithm for Job Scheduling in Cloud Computing", Proceedings of The Third International Conference on Communications and Information Technology, 2012, pp [17] J. Kennedy and R. Eberhart. Particle swarm optimization.in IEEE International Conference on Neural Networks, volume4, pages , [18] W. Qing, and Z. Han-Chao, "Optimization of Task Allocation And Knowledge Workers Scheduling Based-on Particle Swarm Optimization, "In Proceedings of IEEE International Conference on Electric Information and Control Engineering, 2011, pp [19] Jemina Priyadarsini R and Arockiam L, Performance Evaluation of Min-Min and Max-Min algorithms for job scheduling in federated cloud, International Journal of Computer Applications (IJCA),Vol. 99,Number 18 August 2014, ISSN: , pp (IF 0.824)

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