Energy-Aware Scheduling of Tasks in Cloud Computing

被引：0

作者：

Mehor, Yamina ^{[1
]}

Rebbah, Mohammed ^{[1
]}

Smail, Omar ^{[1
]}

机构：

[1] Computer Science Department, University of Mustapha Stambouli Mascara, Mascara

来源：

Informatica (Slovenia) | 2024年 / 48卷 / 16期

关键词：

adaptive genetic algorithm; cloud computing; energy consumption; execution time; SLA violation; task scheduling;

D O I：

10.31449/inf.v48i16.5741

中图分类号：

学科分类号：

摘要：

Cloud computing Infrastructures have been created to facilitate consumer’s access to various services through the Internet. Massive energy consumption by data centers that hosts Cloud applications result in high carbon footprints to the environment. Therefore, it is required to develop ways that reduce the energy consumption. These aspects are reduced by efficiently task scheduling within the deadline respect and providing the resources according to the user’s request. Energy usage, execution time, and SLA violations in virtualized cloud data centers are discussed in this study. For effective scheduling, the suggested approach is predicated on job categorization and thresholds. Tasks having lengthy execution duration are preprocessed in the first stage by being placed in different lists. The following stage involves classifying tasks according to the resources required. Finally, Genetic Algorithm is used to select the best schedules. To represent the dynamic nature of the cloud environment and to offer a scheduling solution that is nearly optimum and decrease energy consumption, execution time, and SLA violation, an adaptive Genetic Algorithm is developed. By the use of cloud infrastructure simulation and a series of performance and quality assessment experiments, the suggested model is tested in this setting. Results show that the suggested method improves performance by reducing execution time, energy usage, and SLA violations. © 2024 Slovene Society Informatika. All rights reserved.

引用

页码：125 / 136

页数：11

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