Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing

• Published in: Sustainable computing informatics and systems Vol. 30; p. 100517
• Main Authors: Hussain, Mehboob, Wei, Lian-Fu, Lakhan, Abdullah, Wali, Samad, Ali, Soragga, Hussain, Abid
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.06.2021
• Subjects: Deadline; Energy consumption; Task scheduling; Virtualized cloud; Task scheduling; Energy consumption; Virtualized cloud; Deadline
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2021.100517

•We build a system architecture that contains master node, task sequencing, initial scheduling, and energy-efficient task reassignment components for process the tasks under their deadlines.•We design a scheduling algorithm to reduce energy consumption in the heterogeneous virtualized cloud.•This study proposes an energy-efficient task priority framework to create a fair balance between task scheduling and energy saving.•The simulation results demonstrate that our proposed method helps to reduce significant energy consumption with the deadline constraint satisfied compared with the existing solution methods. In virtualized cloud computing systems, energy reduction is a serious concern since it can offer many major advantages, such as reducing running costs, increasing system efficiency, and protecting the environment. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping cloud resources to user requests to achieve good performance by minimizing the energy consumption of cloud resources within a user-defined deadline is a huge challenge. This paper proposes Energy and Performance-Efficient Task Scheduling Algorithm (EPETS) in a heterogeneous virtualized cloud to resolve the issue of energy consumption. There are two stages in the proposed algorithm: initial scheduling helps to reduce execution time and satisfy task deadlines without considering energy consumption, and the second stage task reassignment scheduling to find the best execution location within the deadline limit with less energy consumption. Moreover, to make a reasonable balance between task scheduling and energy saving, we suggest an energy-efficient task priority system. The simulation results show that, compared to current energy-efficient scheduling methods of RC-GA, AMTS, and E-PAGA, the proposed solution helps to reduce significant energy consumption and improve performance by 5%–20% with deadline constraint satisfied.