Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy
Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy
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DOI：
10.1109/tpds.2017.2688445
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发表时间：
2018-06
影响因子：
5.3
通讯作者：
Xiang Li;Peter Garraghan;Xiaohong Jiang;Zhaohui Wu;Jie Xu
中科院分区：
文献类型：
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作者：
Xiang Li;Peter Garraghan;Xiaohong Jiang;Zhaohui Wu;Jie Xu
由于通过虚拟化在全球范围内快速部署应用程序和服务，云计算中心消耗的能源急剧增加。通过应用能源感知的虚拟机调度，云提供商能够提高能源效率并降低运营成本。计算中心的能耗包括计算能耗和冷却能耗。然而，由于实际云数据中心操作的能源和热建模的复杂性，传统方法无法为虚拟机调度提供全面深入的解决方案，包括计算和冷却能源。本文通过提出一个详细的热模型来分析气流和服务器CPU的温度分布，从而解决了这一挑战。我们提出GRANITE -一个整体的虚拟机调度算法，能够最大限度地减少总的数据中心的能源消耗。使用从Google数据中心跟踪日志中提取的真实的云工作负载特征，对其他现有的工作负载调度算法MaxUtil、TASA、IQR和Random进行了评估。结果表明，与最先进的技术相比，GRANITE消耗的总能量减少了4.3%-43.6%，并将临界温度违规的概率降低了99.2%，SLA违规率为0.17%。
Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3—43.6 percent less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2 with 0.17 percent SLA violation rate as the performance penalty.