Abstract
The new and emerging IT paradigm, Cloud computing provides different options to customers to compute the tasks’ based on their choice and preference. Cloud systems provide services to customers as a utility. The customers are interested in the availability of service at low cost and minimization of task completion time. The performance of cloud systems depends on efficient scheduling of tasks. When cloud server receives multiple user requests, it is necessary for the service provider to schedule the tasks to the appropriate resources to realize the customer satisfaction. In this paper we propose Multi objective Whale Optimization Algorithm (WOA) to schedule tasks in cloud environment. WOA schedules the tasks based on a fitness parameter. The fitness parameter depends on three major constraints: resource utilization, quality of service and energy. The proposed WOA schedules the tasks based on above three parameters such that the task execution time and cost involved in the execution on virtual machines is minimal. The efficiency of the scheduling algorithm depends on minimum fitness parameter. The experimental results show that proposed WO scheduling algorithm provides superior results when compared with existing algorithms.
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Buyya, R., Pandey, S., Vecchiola, C.: Cloudbus toolkit for market-oriented cloud computing. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) CloudCom 2009. LNCS, vol. 5931, pp. 24–44. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10665-1_4
Pandey, S., Wu, L., Guru,. S, Buyya, R.: Workflow engine for clouds. In: Buya, R., Broberg, J. (eds.) Cloud computing: Principles and Paradigms, pp. 321–344. Wiley Press, New York, February 2011. ISBN-13 978-0470887998
Lia, Z., Gea, J., Yangc, H., Huangd, L., Hue, H., Hua, H., Luoa, B.: A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Future Gener. Comput. Syst. 65, 140–152 (2016)
Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)
Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015)
Ahmada, S.G., Liewa, C.S., Munirb, E.U., Anga, T.F., Khanc, S.U.: A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J. Parallel Distrib. Comput. 87, 80–90 (2016)
Zhong, Z., Chen, K., Zhai, X., Zhou, S.: Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Sci. Technol. 21(6), 660–667 (2016)
Netjinda, N., Sirinaovakul, B., Achalakul, T.: Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization. J. Supercomput. 68(3), 1579–1603 (2014)
Ananth, A., Chandrasekaran, K.: Cooperative game theoretic approach for job scheduling in cloud computing. In: 2015 International Conference on Computing and Network Communications (CoCoNet), pp. 147–156, February 2016
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Tareghian, S., Bornaee, Z.: Algorithm to improve job scheduling problem in cloud computing environment. In: 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), pp. 684–688 (2015)
Tao, F., Li, C., Liao, T.W., Laili, Y.: BGM-BLA: a new algorithm for dynamic migration of virtual machines in cloud computing. IEEE Trans. Serv. Comput. 9(6), 910–925 (2015)
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Narendrababu Reddy, G., Kumar, S.P. (2018). Multi Objective Task Scheduling Algorithm for Cloud Computing Using Whale Optimization Technique. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_22
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DOI: https://doi.org/10.1007/978-981-10-8657-1_22
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