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An efficient VM scheduling algorithm to minimize the makespan and maximize the profit


Citation

Ibrahim, Aws Fadhil (2019) An efficient VM scheduling algorithm to minimize the makespan and maximize the profit. Masters thesis, Universiti Putra Malaysia.

Abstract

Virtual Machines (VMs) in Cloud systems are scheduled to host based on the usage of instant resource, namely hosts that are equipped with the highest RAM that is available. This is done without taking their long-term and overall utilization into account. The main issue of VM scheduling is that, it lowers the performance of a system. It is used to schedule tasks for better utilization of resources by allocating certain tasks to particular resources at a particular time. VM scheduling in cloud means to select the most suitable and outstanding resource attainable for execution of tasks or to appoint computer machined to task in such a method that the fulfillment time is reduced as workable. In this study, we focus on improving the scheduling performance of VM, namely on the cost and makespan. The process of VM scheduling includes three main processes. The first process is VM cluster formation based on the characteristics such as CPU, Memory and bandwidth. The second process is hyper analytical task scheduling algorithm, and based on the scheduled task, the policy based profit maximization algorithm was proposed in final process. The comparison of the performance of proposed work is analyzed through some empirical results. The findings have demonstrated that the proposed work has decreased the task scheduling makespan significantly and gives high profit compared with the other scheduling algorithms.


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Additional Metadata

Item Type: Thesis (Masters)
Subject: Cloud computing
Subject: Virtual computer systems
Call Number: FSKTM 2019 27
Chairman Supervisor: Mr. Ahmad Alauddin Ariffin
Divisions: Faculty of Computer Science and Information Technology
Depositing User: Mas Norain Hashim
Date Deposited: 23 Jul 2020 04:07
Last Modified: 23 Jul 2020 04:07
URI: http://psasir.upm.edu.my/id/eprint/82948
Statistic Details: View Download Statistic

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