An intelligent energy-efficient approach for managing IoE tasks in cloud platforms

被引：24
作者：
Javadpour A. [1 ,2 ,5 ]
Nafei A.H. [3 ]
Ja’fari F. [4 ]
Pinto P. [5 ]
Zhang W. [1 ]
Sangaiah A.K. [6 ]
机构：
[1] Department of Computer Science and Technology (Cyberspace Security), Harbin Institute of Technology, Shenzhen
[2] ADiT-Lab, Electrotechnics and Telecommunications Department, Instituto Politécnico de Viana do Castelo, Porto
[3] Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei
[4] Department of Computer Engineering, Sharif University of Technology, Tehran
[5] Electrotechnics and Telecommunications Department, Instituto Politécnico de Viana do Castelo, Porto
[6] International Graduate Institute of AI, National Yunlin University of Science and Technology, Douliu
来源：
Journal of Ambient Intelligence and Humanized Computing | 2023年 / 14卷 / 4期
基金：
中国国家自然科学基金;
关键词：
Artificial Intelligence; Cloud computing; DVFS; Green computing; Internet of Everything (IoE); Microgenetic; Score function; Tasks scheduling;
D O I：
10.1007/s12652-022-04464-x
中图分类号：
学科分类号：
摘要：
Today, cloud platforms for Internet of Everything (IoE) are facilitating organizational and industrial growth, and have different requirements based on their different purposes. Usual task scheduling algorithms for distributed environments such as group of clusters, networks, and clouds, focus only on the shortest execution time, regardless of the power consumption. Network energy can be optimized if tasks are properly scheduled to be implemented in virtual machines, thus achieving green computing. In this research, Dynamic Voltage Frequency Dcaling (DVFS) is used in two different ways, to select a suitable candidate for scheduling the tasks with the help of an Artificial Intelligence (AI) approach. First, the GIoTDVFS_SFB method based on sorting processor elements in Cloud has been considered to handle Task Scheduling problem in the Clouds system. Alternatively, the GIoTDVFS_mGA microgenetic method has been used to select suitable candidates. The proposed mGA and SFB methods are compared with SLAbased suggested for Cloud environments, and it is shown that the Makespan and Gain in benchmarks 512 and 1024 are optimized in the proposed method. In addition, the Energy Consumption (EC) of Real PM (RPMs) against the numeral of Tasks has been considered with that of PAFogIoTDVFS and EnergyAwareDVFS methods in this area. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
引用
收藏
页码：3963 / 3979
页数：16
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