The International Arab Journal of Information Technology (IAJIT)


An Improved Grey Wolf Optimization Algorithm Based Task Scheduling in Cloud Computing

The demand for massive computing power and storage space has been escalating in various fields and in order to satisfy this need a new technology known as cloud computing is introduced. The capability of providing these services effectively and economically has made cloud computing technology more popular. With the advent of virtualization, IT services being offered have started to shift to cloud computing. Virtualization had paved way for resource availability in an inexhaustible manner. As Cloud Computing is still at its unrefined form and to derive its full potential more analysis is needed. The way in which resources and tasks get allocated in cloud environment requires more analysis. This in turn accounts for the Quality of Services (QoS) of the services offered by cloud service providers. This paper proposes to simulate the Performance- Cost Grey Wolf Optimization (PCGWO) algorithm based to achieve optimization in the process of allocation of resources and tasks in cloud computing domain using CloudSim toolkit. The main purpose is to lower both the processing time and cost in accordance to objective function. The superiority of proposed technique is evident from the simulation results that show a comprehensive reduction in task completion time and cost. Also using this technique more no. of tasks can be efficiently completed within the deadline. Thus the results indicate that in accordance to performance the PCGWO method fares better than existing algorithms.

[1] Abdullahi M. and Ngadi M., “Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment,” PloS one, vol. 11, no. 6, pp. 1-29, 2016.

[2] Abdullahi M., Ngadi M., and Abdulhamid S., “Symbiotic Organism Search Optimization Based Task Scheduling in Cloud Computing Environment,” Future Generation Computer Systems, vol. 56, pp. 640-650, 2016.

[3] Abramson D., Buyya R., and Giddy J., “A Computational Economy for Grid Computing and its Implementation in the Nimrod-G Resource Broker,” Future Generation Computer Systems, vol. 18, no. 8, pp. 1061-1074, 2002.

[4] Babaeizadeh S. and Ahmad R., “Enhanced Constrained Artificial Bee Colony Algorithm for Optimization Problems,” The International Arab Journal of Information Technology, vol. 14, no. 2, pp. 246-253, 2017.

[5] Buyya R., Yeo C., Venugopal S., Broberg J., and Brandic I., “Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.

[6] Carretero J., Xhafa F., and Abraham A., “Genetic Algorithm Based Schedulers for Grid Computing Systems,” International Journal of Innovative Computing, Information and Control, vol. 3, no. 6, pp. 1-19, 2007.

[7] Cheng M. and Prayogo D., “Symbiotic Organisms Search: A New Metaheuristic Optimization Algorithm,” Computers and Structures, vol. 139, pp. 98-112, 2014.

[8] Dorigo M., Birattari M., and Stutzle T., “Ant Colony Optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, 2006.

[9] Ergu D., Kou G., Peng Y., Shi Y., and Shi Y., “The Analytic Hierarchy Process: Task Scheduling and Resource Allocation in Cloud Computing Environment,” The Journal of Supercomputing, vol. 64, no. 3, pp. 835-848, 2013.

[10] Grandinetti L., Pisacane O., and Sheikhalishahi M., “An Approximate ϵ-Constraint Method for a Multi-Objective Job Scheduling in The Cloud,” Future Generation Computer Systems, vol. 29, no. 8, pp. 1901-1908, 2013.

[11] Jiao H., Zhang J., Li J., Shi J., and Li J., “Immune Optimization of Task Scheduling on Multidimensional Qos Constraints,” Cluster Computing, vol. 18, no. 2, pp. 909-918, 2015.

[12] Kalra M. and Singh S., “A Review of Metaheuristic Scheduling Techniques in Cloud Computing,” Egyptian Informatics Journal, vol. 16, no. 3, pp. 275-295, 2015.

[13] Liu Y., Zhang C., Li B., and Niu J., “DeMS: A Hybrid Scheme of Task Scheduling and Load Balancing in Computing Clusters,” Journal of Network and Computer Applications, vol. 83, no. 1, pp. 213-220, 2017.

[14] Mirjalili S., Saremi S., Mirjalili S., and Coelho L., “Multi-objective Grey Wolf Optimizer: A Novel Algorithm for Multi-Criterion Optimization,” Expert Systems with Applications, vol. 47, pp. 106-119, 2016.

[15] Natesan G. and Chokkalingam A., “Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment,” International Journal of Intelligent Engineering and Systems, vol. 10, no. 1, pp. 186-195, 2017.

[16] Pandey S., Wu L., Guru S., 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, Perth, pp. 400-407, 2010.

[17] Selvarani S. and Sadhasivam G., “An intelligent Water Drop Algorithm for Optimizing Task Scheduling in Grid Environment,” The International Arab Journal of Information Technology, vol. 13, no. 6, pp. 627-634, 2016.

[18] Somasundaram T. and Govindarajan K., “CLOUDRB: A Framework for Scheduling and Managing High-Performance Computing Applications in Science Cloud,” Future Generation Computer Systems, vol. 34, pp. 47- 65, 2014.

[19] Song B., Hassan M., and Huh E., “A Novel Heuristic-Based Task Selection and Allocation Framework in Dynamic Collaborative Cloud Service Platform,” in Proceeding of 2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Indianapolis, pp. 360-367, 2010.

[20] Tsai J., Fang J., and Chou J., “Optimized Task Scheduling and Resource Allocation on Cloud Computing Environment Using Improved Differential Evolution Algorithm,” Computers and Operations Research, vol. 40, no. 12, pp. 3045-3055, 2013.

[21] Verma A. and Kaushal S., “Deadline Constraint Heuristic-Based Genetic Algorithm for Workflow Scheduling in Cloud,” International Journal of Grid and Utility Computing, vol. 5, no. 2, pp. 96-106, 2014.

[22] Wei G., Vasilakos A., Zheng Y., and Xiong N., “A Game-Theoretic Method of Fair Resource Allocation for Cloud Computing Services,” The An Improved Grey Wolf Optimization Algorithm Based Task Scheduling ... 81 Journal of Supercomputing, vol. 54, no. 2, pp. 252-269, 2010.

[23] Xu B., Zhao C., Hu E., and Hu B., “Job Scheduling Algorithm Based on Berger Model in Cloud Environment,” Advances in Engineering Software, vol. 42, no. 7, pp. 419-425, 2011.

[24] Xu J., Lam A., and Li V., “Chemical Reaction Optimization for Task Scheduling in Grid Computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 10, pp. 1624- 1631, 2011.

[25] Yao G., Ding Y., Jin Y., and Hao K., “Endocrine-Based Coevolutionary Multi-Swarm for Multi-Objective Workflow Scheduling in A Cloud System,” Soft Computing, vol. 21, no. 15, pp. 4309-4322, 2017.

[26] Yu J. and Buyya R., “Scheduling Scientific Workflow Applications with Deadline and Budget Constraints Using Genetic Algorithms,” Scientific Programming, vol. 14, no. 3-4, pp. 217-230, 2006.

[27] Zhang Q., Cheng L., and Boutaba R., “Cloud Computing: State-Of-The-Art and Research Challenges,” Journal of Internet Services and Applications, vol. 1, no. 1, pp. 7-18, 2010.

[28] Zhong H., Tao K., and Zhang X., “An Approach To Optimized Resource Scheduling Algorithm for Open-Source Cloud Systems,” in Proceedings of 5th Annual IEEE ChinaGrid Conference, Guangzhou, pp. 124-129, 2010.

[29] Zuo L., Shu L., Dong S., Zhu C., and Hara T., “A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing,” IEEE Access, vol. 3, pp. 2687-2699, 2015.

[30] 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. Gobalakrishnan Natesan pursued his Bachelor’s degree in Information Technology at Anna University, Tamilnadu, India in 2005. Then he obtained his Master’s degree in Software Engineering from Bharathidhasan University, Tamilnadu, India in 2008. Currently, he is a research scholar in Sathyabama University Chennai, India and he is an Assistant Professor in the Department of Information Technology, St.Joseph’s College of Engineering, Tamilnadu, India. His current research interests are Cloud computing, Virtualization and Big Data. Arun Chokkalingam received his Ph.D. from Anna University, Chennai, India. Currently, he is working as a Professor in the Department of Electronics and Communication Engineering, R.M.K College of Engineering and Technology, Tamilnadu, India. He has more than 15 years of teaching experience in Engineering College. He has published many papers in International Conferences, National & International Journal in areas such as cloud computing, VLSI, image processing and wireless sensor. His current research interests are Cloud Computing, VLSI and Optimization Techniques. He is a life time member of ISTE.