An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data
An energy‐aware virtual machine scheduling method for service QoS enhancement in clouds over big data
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DOI：
10.1002/cpe.3909
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Wanchun Dou;Xiaolong Xu;Shunmei Meng;Xuyun Zhang;Chunhua Hu;Shui Yu;Jian Yang
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文献类型：
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作者：
Wanchun Dou;Xiaolong Xu;Shunmei Meng;Xuyun Zhang;Chunhua Hu;Shui Yu;Jian Yang
由于大数据对物理资源的强烈需求，在云中存储和处理大数据是一种有效和高效的方式，因为云计算允许按需资源配置。随着对云平台提供的资源的需求不断增加，用于大数据管理的云服务的服务质量（QoS）变得非常重要。大数据具有稀疏性的特点，导致数据访问和处理的频繁性，从而造成巨大的能源消耗。能源成本在确定服务价格方面起着关键作用，应该像其他QoS指标一样被视为一等公民，因为节能服务可以实现更便宜的服务价格和环保的解决方案。然而，以能量感知的方式有效地调度虚拟机（VM）以用于服务QoS增强仍然是一个挑战。在本文中，我们提出了一种能量感知的动态VM调度方法，用于在大数据云上提高QoS，以解决上述挑战。具体而言，该方法包括两个主要的VM迁移阶段，其中计算任务被迁移到具有较低能耗或较高性能的服务器，以降低服务价格和执行时间。广泛的实验评估表明，我们的方法的有效性和效率。版权所有© 2016约翰威利父子有限公司.
Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on‐demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first‐class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy‐aware manner. In this paper, we propose an energy‐aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.