Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee
Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee
复制标题
云端自适应任务调度策略：当能耗满足性能保证时
DOI：
10.1007/s11280-016-0382-4
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发表时间：
2016-02
影响因子：
3.7
通讯作者：
Shen Jian
中科院分区：
文献类型：
--
作者：
Shen Yao;Bao Zhifeng;Qin Xiaolin;Shen Jian
云计算的能源效率比以往任何时候都受到了极大的关注。挑战之一是如何在最小化能量消耗和满足服务质量（例如及时地满足性能和资源可用性）之间取得平衡。许多基于在线迁移技术的研究试图将虚拟机从低利用率的主机中迁移出来，然后将其关闭，以降低能耗。在本文中，我们的目标是开发一个自适应的任务调度策略。特别地，本文首先从云任务调度的角度对虚拟机能耗进行建模，然后提出了一种遗传算法来实现对云任务中不同能耗和性能要求的自适应调节（E-PAGA）。然后，我们设计了两种类型的适应度函数选择下一代的不同偏好的能量和性能。因此，我们可以自适应地调整能源和性能目标之前，分配的任务在云中，这是能够满足不同的用户的各种需求。通过大量的实验，我们发现了几个对真实的云数据中心的配置非常有用的重要结论：1）我们证明了保证最小总任务时间通常会在一定程度上导致低能耗; 2）如果只考虑能量优化，我们必须付出牺牲性能的代价; 3）得出云数据中心总存在能效比最优的条件，更重要的是可以得到最优能效比的具体条件。
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.
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DOI：
10.1007/978-3-642-30154-4_10
发表时间：
2012
期刊：
--
影响因子：
--
作者：
J. Kolodziej;S. Khan;Albert Y. Zomaya
通讯作者：
J. Kolodziej;S. Khan;Albert Y. Zomaya
DOI：
--
发表时间：
2010
期刊：
--
影响因子：
--
作者：
Ian T Foster;Borja Sotomayor Basilio
通讯作者：
Ian T Foster;Borja Sotomayor Basilio
DOI：
10.1109/cluster.2010.15
发表时间：
2010-09
期刊：
2010 IEEE International Conference on Cluster Computing
影响因子：
--
作者：
Íñigo Goiri;F. Julià;Ramon Nou;J. L. Berral;Jordi Guitart;J. Torres
通讯作者：
Íñigo Goiri;F. Julià;Ramon Nou;J. L. Berral;Jordi Guitart;J. Torres
影响因子：
22.7
作者：
S. Albers
通讯作者：
S. Albers
DOI：
10.1109/hotos.2001.990063
发表时间：
2001-05
期刊：
Proceedings Eighth Workshop on Hot Topics in Operating Systems
影响因子：
--
作者：
R. Neugebauer;D. McAuley
通讯作者：
R. Neugebauer;D. McAuley