DeepPM: Efficient Power Management in Edge Data Centers using Energy Storage
DeepPM: Efficient Power Management in Edge Data Centers using Energy Storage
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DOI：
10.1109/cloud49709.2020.00058
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Zhihui Shao;Mohammad A. Islam;Shaolei Ren
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文献类型：
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作者：
Zhihui Shao;Mohammad A. Islam;Shaolei Ren
随着物联网（IoT）的快速发展，计算工作负载逐渐向低延迟的互联网边缘移动。由于工作负载的显著波动，在分布式位置构建的边缘数据中心会遭受资源利用不足的问题，并且需要容量不足以避免浪费资本投资。然而，工作负载的波动也使边缘数据中心更适合电池辅助电源管理，以应对由于配置不足而造成的性能影响。特别是，工作负载波动允许电池频繁充电并可用于临时容量提升。但是，使用电池可能会使数据中心冷却系统过载，该系统设计有与电力系统匹配的容量。在本文中，我们设计了一种新型的电源管理解决方案DeepPM，该解决方案利用边缘数据中心内的UPS电池和冷空气作为能量存储来提高性能。DeepPM使用深度强化学习（DRL）以无模型的方式在线学习数据中心的热行为，并在运行中使用它来确定功率分配，以实现最佳延迟性能，而不会使数据中心过热。我们的评估表明，与功率封顶基线相比，DeepPM可以将延迟性能提高50%以上，同时服务器入口温度保持在安全操作限制范围内（例如，32°C）。
With the rapid development of the Internet of Things (IoT), computational workloads are gradually moving toward the internet edge for low latency. Due to significant workload fluctuations, edge data centers built in distributed locations suffer from resource underutilization and requires capacity underprovisioning to avoid wasting capital investment. The workload fluctuations, however, also make edge data centers more suitable for battery-assisted power management to counter the performance impact due to underprovisioning. In particular, the workload fluctuations allow the battery to be frequently recharged and made available for temporary capacity boosts. But, using batteries can overload the data center cooling system which is designed with a matching capacity of the power system. In this paper, we design a novel power management solution, DeepPM, that exploits the UPS battery and cold air inside the edge data center as energy storage to boost the performance. DeepPM uses deep reinforcement learning (DRL) to learn the data center thermal behavior online in a model-free manner and uses it on-the-fly to determine power allocation for optimum latency performance without overheating the data center. Our evaluation shows that DeepPM can improve latency performance by more than 50% compared to a power capping baseline while the server inlet temperature remains within safe operating limits (e.g., 32°C).