Energy-Aware Scheduling in Virtualized Datacenters
Energy-Aware Scheduling in Virtualized Datacenters
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DOI：
10.1109/cluster.2010.15
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发表时间：
2010-09
期刊：
影响因子：
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通讯作者：
Íñigo Goiri;F. Julià;Ramon Nou;J. L. Berral;Jordi Guitart;J. Torres
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文献类型：
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作者：
Íñigo Goiri;F. Julià;Ramon Nou;J. L. Berral;Jordi Guitart;J. Torres
通过广泛使用虚拟化技术，可以将多个工作负载整合到数量较少的机器中，从而实现大规模数据中心能耗的降低。然而，虚拟化也会带来一些额外的开销（例如虚拟机创建和迁移），这些开销可能会影响最佳整合配置，因此必须将其考虑在内。在本文中，我们提出了一个动态的作业调度策略，在虚拟化数据中心的电源感知资源分配。我们的策略试图将来自不同机器的工作负载整合到数量较少的节点中，同时满足保持每个作业的服务质量所需的硬件资源量。这允许关闭备用服务器，从而降低整体数据中心功耗。作为一种新奇，该策略将所有虚拟化开销纳入决策过程。此外，我们的策略还准备考虑数据中心的其他重要参数，例如可靠性或动态SLA实施，并与功耗协同工作。引入的策略进行评估，比较它对常见的策略在一个模拟的环境中，准确地模拟HPC作业执行的虚拟化数据中心，包括功耗建模，并获得了15%的功耗降低相对于典型的政策。
The reduction of energy consumption in large-scale datacenters is being accomplished through an extensive use of virtualization, which enables the consolidation of multiple workloads in a smaller number of machines. Nevertheless, virtualization also incurs some additional overheads (e.g. virtual machine creation and migration) that can influence what is the best consolidated configuration, and thus, they must be taken into account. In this paper, we present a dynamic job scheduling policy for power-aware resource allocation in a virtualized datacenter. Our policy tries to consolidate workloads from separate machines into a smaller number of nodes, while fulfilling the amount of hardware resources needed to preserve the quality of service of each job. This allows turning off the spare servers, thus reducing the overall datacenter power consumption. As a novelty, this policy incorporates all the virtualization overheads in the decision process. In addition, our policy is prepared to consider other important parameters for a datacenter, such as reliability or dynamic SLA enforcement, in a synergistic way with power consumption. The introduced policy is evaluated comparing it against common policies in a simulated environment that accurately models HPC jobs execution in a virtualized datacenter including power consumption modeling and obtains a power consumption reduction of 15% with respect to typical policies.