用于有效数据中心规划和计算管理的功耗建模
Power Modeling for Effective Datacenter Planning and Compute Management
摘要：
数据中心的电力需求一直在不断增长，是其成本的关键驱动因素。计算资源（CPU、RAM等）的准确映射和硬件类型（服务器、加速器等）降低功耗已成为主要Web和云服务提供商的关键要求。随着数据中心容量和相关功耗的全球增长，这些模型对于围绕数据中心设计和操作的重要决策至关重要。在本文中，我们讨论了两类统计功率模型，这些模型被设计和验证为准确、简单、可解释且适用于Google舰队超大规模数据中心的所有硬件配置和工作负载。据我们所知，这是此类规模最大的功耗建模研究，涉及各种数据中心规划和实时管理用例，以及用于建模和验证的各种硬件配置和工作负载类型。我们证明，所提出的统计建模技术，而简单和可扩展的，预测功率小于5%的平均绝对百分比误差（MAPE）超过95%的不同配电单元（超过2000），仅使用4个功能。这种性能与之前最先进方法的报告准确性相匹配，同时使用的功能明显减少，并覆盖了更广泛的用例。
英文摘要：
Datacenter power demand has been continuously growing and is the key driver of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and hardware types (servers, accelerators, etc.) to power consumption has emerged as a critical requirement for major Web and cloud service providers. With the global growth in datacenter capacity and associated power consumption, such models are essential for important decisions around datacenter design and operation. In this paper, we discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads across hyperscale datacenters of Google fleet. To the best of our knowledge, this is the largest scale power modeling study of this kind, in both the scope of diverse datacenter planning and real-time management use cases, as well as the variety of hardware configurations and workload types used for modeling and validation. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features. This performance matches the reported accuracy of the previous started-of-the-art methods, while using significantly less features and covering a wider range of use cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
