An Integer Linear Programming model and Adaptive Genetic Algorithm approach to minimize energy consumption of Cloud computing data centers
An Integer Linear Programming model and Adaptive Genetic Algorithm approach to minimize energy consumption of Cloud computing data centers
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DOI：
10.1016/j.compeleceng.2018.02.028
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Huda Ibrahim;Raafat Aburukba;K. El-Fakih
中科院分区：
文献类型：
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作者：
Huda Ibrahim;Raafat Aburukba;K. El-Fakih
云计算基础设施旨在支持消费者通过互联网获得各种服务。托管云应用程序的数据中心消耗大量电力，对环境造成高碳足迹。因此，需要解决方案来最小化能量消耗。本文通过提出一种最小化云数据中心能耗的线性规划模型，重点研究了一种动态任务调度算法。此外，提出了一种自适应遗传算法（GA），以反映云环境的动态特性，并提供一个接近最佳的调度解决方案，最大限度地减少能源消耗。通过模拟云基础设施并在此环境下进行一系列性能和质量评估研究，对所提出的自适应遗传算法进行了验证。结果表明，所提出的解决方案在响应时间和降低能耗方面提供了性能增益。
Cloud computing infrastructures are designed to support the accessibility and availability of various services to consumers over the Internet. Data centers hosting Cloud applications consume massive amount of power, contributing to high carbon footprints to the environment. Hence, solutions are needed to minimize the energy consumption. This paper focuses on the development of a dynamic task scheduling algorithm by proposing an Integer Linear Programming (ILP) model that minimizes the energy consumption in a Cloud data center. Furthermore, an Adaptive Genetic Algorithm (GA) is proposed to reflect the dynamic nature of the Cloud environment and to provide a near optimal scheduling solution that minimizes the energy consumption. The proposed adaptive GA is validated by simulating the Cloud infrastructure and conducting a set of performance and quality evaluation study in this environment. The results demonstrate that the proposed solution offers performance gains with regards to response time and in reducing energy consumption.