The International Arab Journal of Information Technology (IAJIT)


Self-Adaptive PSO Memetic Algorithm For Multi Objective Workflow Scheduling in Hybrid Cloud

Cloud computing is a technology in distributed computing that facilitate pay per model to solve large scale problems. The main aim of cloud computing is to give optimal access among the distributed resources. Task scheduling in cloud is the allocation of best resource to the demand considering the different parameters like time, makespan, cost, throughput etc. All the workflow scheduling algorithms available cannot be applied in cloud since they fail to integrate the elasticity and heterogeneity in cloud. In this paper, the cloud workflow scheduling problem is modeled considering make span, cost, percentage of private cloud utilization and violation of deadline as four main objectives. Hybrid approach of Particle Swarm Optimization (PSO) and Memetic Algorithm (MA) called Self-Adaptive Particle Swarm Memetic Algorithm (SPMA) is proposed. SPMA can be used by cloud providers to maximize user quality of service and the profit of resource using an entropy optimization model. The heuristic is tested on several workflows. The results obtained shows that SPMA performs better than other state of art algorithms.

[1] Al-Maamari A. and Omara F., “Task Scheduling Using PSO Algorithm in Cloud Computing Environments,” International Journal of Grid Distribution Computing, vol. 8, no. 5, pp. 245- 256, 2015.

[2] Chen W. and Zhang J., “An Ant Colony Optimization Approach to A Grid Workflow Scheduling Problem with Various Qos Requirements,” IEEE Transaction System Man Cybern, vol. 39, no. 1, pp. 29-43, 2009.

[3] Durillo J. and Pordan R., “Multi Objective Workflow Scheduling in Amazon EC2,” Cluster Computing, vol. 17, no. 2, pp. 169-189, 2014.

[4] Garg R. and Singh A., “Multiobjective Workflow Grid Scheduling Based on Discrete Particle Swarm Optimization,” in Proceedings of International Conference on Swarm, Evolutionary, and Memetic Computing, Visakhapatnam, pp. 183-190, 2011.

[5] John S. and Mohamed M., “A Network Performance Aware QoS Based Workflow Scheduling for Grid Services,” The International Arab Journal of Information Technology, vol. 15, no. 5, pp. 894-903, 2018.

[6] Kang F. and Li J., “Artificial Bee Colony Algorithm Optimized Sup Port Vector Regression for System Reliability Analysis of Slopes,” Journal of Computing in Civil Engineering, vol. 30, no. 3, pp. 04015040, 2016.

[7] Kang F., Li J., and LiJ J., “System Reliability Analysis of Slopes Using Least Squares Support Vector Machines with Particle Swarm Optimization,” Neurocomputing, vol. 209, pp. 46-56, 2016.

[8] Kennedy J. and Eberhart R., “Particle Swarm Optimization,” in Proceedings of the 6th IEEE International Conference in Neural Network, Perth, pp. 1942-1928, 1995.

[9] Sahni J. and Vidyarthi D., “A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment,” IEEE Transactions on Cloud Computing, vol. 6, pp. 2-18, 2016.

[10] Sakellaiou R., Zaho H., Tsiakkouri E., and Dikaiakos M., Integrated Research in GRID Computing, Springer, 2007.

[11] Sharkh M., Shami A., and Ouda A., “Optimal and Suboptimal Resource Allocation Techniques in Cloud Computing Data Centers,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 6, no. 1, pp. 1-17, 2017.

[12] Soniya J., Sujana J., and Revathi T., “IEEE Dynamic Fault Tolerant Scheduling Mechanism for Real Time Tasks in Cloud Computing,” in Proceedings of International Conference on Electrical, Electronics, and Optimization Techniques, Chennai, pp. 124-129, 2016.

[13] Tawfeek M., El-Sisi A., Keshk A., and Torkey F., “Cloud Task Scheduling Based on Ant Colony Optimization,” The International Arab Journal of Information Technology, vol. 12, no. 2, pp. 129-137, 2015.

[14] Yu J., Kirle y., and Buyya R., “Multiobjective Planning for Workflow Execution on Grids,” in Proceedings of the 8th IEEE/ ACM International Conference on Grid Computing, Austin, pp. 10- 17, 2007.

[15] Zhang F., Cao J., Hwang K., and Wu C., ”Ordinal Optimized Scheduling Of Scientific Workflows in Elastic Compute Clouds,” in Proceedings of the 3rd IEEE International Conference on Cloud Computing Technology and Science, Athens, pp. 9-17, 2011.

[16] Zuo X., Zhang G., and Tan W., “Self Adaptive Learning PSO- Based Deadline Constrained Task Scheduling for Hybrid IaaS Cloud,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 2, pp. 564-573, 2014. Self-Adaptive PSO Memetic Algorithm For Multi Objective Workflow Scheduling... 935 Padmaveni Krishnan received ME degree in computer Science from Madurai Kamaraj University in 2002. She is currently an Assistant Professor in Hindustan Institute of Technology and Science and her main interest are in virtualization and scheduling in cloud. John Aravindhar received PhD degree in Data mining from Hindustan University. He is currently an Associate Professor in Computer Science Department of Hindustan Institute of Technology and Science. His area of interest are Data mining and cloud.