点击次数：
所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
关键字：Cloud computing Cloud task scheduling Energy-aware optimization Genetic algorithms
摘要：Energy efficiency of cloud computing has been given great attention more than ever before. One of the challenges is how to strike a balance between minimizing the energy consumption and meeting the quality of services such as satisfying performance and resource availability in a timely manner. Many studies based on the online migration technology attempt to move virtual machine from low utilization of hosts and then switch it off with the purpose of reducing energy consumption. In this paper, we aim to develop an adaptive task scheduling strategy. In particular, we first model the virtual machine energy from the perspective of the cloud task scheduling, then we propose a genetic algorithm to achieve adaptive regulations for different requirements of energy and performance in cloud tasks (E-PAGA). Then we design two types of the fitness function for choosing the next generation with different preferences on energy and performance. As a result, we can adaptively adjust the energy and performance target before assigning the task in cloud, which is able to meet various requirements from different users. From the extensive experiments, we pinpoint several important observations which are useful in configuring real cloud data centers: 1) we prove that guaranteeing the minimum total task time usually leads to low energy consumption to some extent; 2) we must pay the price of the sacrificed performance if only taking into account the energy optimization; 3) we come to the conclusion that there is always an optimal condition of energy-efficiency ratio in the cloud data center, and more importantly the specific conditions of the optimal energy-efficiency ratio can be obtained.
ISSN号：1386-145X
是否译文：否
发表时间：2017-03-01
合写作者：Shen, Yao,Bao, Zhifeng,Shen, Jian
通讯作者：秦小麟