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Multi Objective Task Scheduling Algorithm for Cloud Computing Using Whale Optimization Technique

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 827))

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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Correspondence to G. Narendrababu Reddy .

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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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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8656-4

  • Online ISBN: 978-981-10-8657-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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