Abstract
In the cloud environment, the process of task scheduling and resource allocation plays a vital role in cloud resource management. The unpredictable and uncertain behaviour of the task arrival rate poses significant challenges in the effective allocation of resources. An efficient scheduling technique is essential to avoid under or overutilization of resources. In order to increase the performance of scheduling and allocation, this paper presents multi-objective optimization method for optimal resource allocation and task scheduling based on a three-stage strategy. In the first stage, a description of tasks and virtual machines is prepared. At stage two, tasks are classified and labelled based on the resource demand and execution time. Finally, the modified-Grey Wolf optimization algorithm is used for the allocation and scheduling of tasks for a disparate scenario. The experimental results proved that the proposed method reduced the makespan time and cost with an improved utilization rate.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
