Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement
Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement
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DOI：
10.1109/isads.2013.6513411
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发表时间：
2013-03
期刊：
影响因子：
--
通讯作者：
Ismael Solís Moreno;Renyu Yang;Jie Xu;Tianyu Wo
中科院分区：
文献类型：
--
作者：
Ismael Solís Moreno;Renyu Yang;Jie Xu;Tianyu Wo
虚拟化是用于提高数据中心资源效率的主要技术之一;它允许在相同的硬件基础设施上部署共存的计算环境。但是，环境的共存--沿着管理效率低下--通常会在运行的工作负载之间造成资源的高度竞争，从而导致性能下降。这种现象被称为性能干扰，并引入了影响数据中心的服务质量和能源效率的不可忽略的开销。本文介绍了一种新的工作负载分配方法，该方法通过考虑工作负载的异构性来提高云计算中心的能源效率。我们使用从真实的云环境中识别的工作负载特征来分析性能干扰对能效的影响，并开发了一个模型，该模型智能地实现各种决策技术，以根据其内部干扰水平来选择最佳的工作负载主机。我们的实验结果表明，减少干扰的27.5%，提高能源效率高达15%，而目前的工作负载分配机制。
Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.