Adaptive Power and Resource Management Techniques for Multi-threaded Workloads
Adaptive Power and Resource Management Techniques for Multi-threaded Workloads
复制标题
多线程工作负载的自适应电源和资源管理技术
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
A. Coskun
中科院分区：
文献类型：
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作者：
Can Hankendi;A. Coskun
随着当今的计算趋势正在向云发展，满足不断增长的计算需求，同时最大限度地降低数据中心的能源成本已变得至关重要。这项工作介绍了两种自适应技术，以减少能源消耗的计算集群，通过多核处理器上的电源和资源管理。我们首先提出了一种新的功率封顶技术来约束计算节点的功耗。我们的技术结合了动态电压频率缩放（DVFS）和多核系统上的线程分配。通过利用机器学习技术，我们的功率封顶方法能够在82%的时间内满足功率预算，而无需任何功率测量设备，与最先进的技术相比，平均降低了51.6%的能耗。然后，我们介绍了一个自治的资源管理技术，整合多线程的工作负载运行在多核服务器上。我们的技术首先分类的应用程序，根据其能源效率的措施，然后按比例分配资源的共同调度的应用程序，以提高能源效率。与最先进的协同调度策略相比，所提出的技术将能源效率提高了17%。
As today's computing trends are moving towards the cloud, meeting the increasing computational demand while minimizing the energy costs in data centers has become essential. This work introduces two adaptive techniques to reduce the energy consumption of the computing clusters through power and resource management on multi-core processors. We first present a novel power capping technique to constrain the power consumption of computing nodes. Our technique combines Dynamic Voltage-Frequency Scaling (DVFS) and thread allocation on multi-core systems. By utilizing machine learning techniques, our power capping method is able to meet the power budgets 82% of the time without requiring any power measurement device and reduces the energy consumption by 51.6% on average in comparison to the state-of-the-art techniques. We then introduce an autonomous resource management technique for consolidated multi-threaded workloads running on multi-core servers. Our technique first classifies applications according to their energy efficiency measure, then proportionally allocates resources for co-scheduled applications to improve the energy efficiency. The proposed technique improves the energy efficiency by 17% in comparison to state-of-the-art co-scheduling policies.