Due to the increasing demand of cloud resources, the ever-increasing number and scale of cloud data centers make their massive power consumption a prominent issue today. Evidence reveals that the behaviors of cloud servers make the major impact on data centers’ power consumption. Although extensive research can be found in this context, a systematic review of the models and modeling methods for the entire hierarchy (from underlying hardware components to the upper-layer applications) of the cloud server is still missing, which is supposed to cover the relevant studies on physical and virtual cloud server instances, server components, and cloud applications. In this article, we summarize a broad range of relevant studies from three perspectives: power data acquisition, power models, and power modeling methods for cloud servers (including bare-metal, virtual machine (VM), and container instances). We present a comprehensive taxonomy on the collection methods of server-level power data, the existing mainstream power models at multiple levels from hardware to software and application, and commonly used methods for modeling power consumption including classical regression analysis and emerging methods like reinforcement learning. Throughout the work, we introduce a variety of models and methods, illustrating their implementation, usability, and applicability while discussing the limitations of existing approaches and possible ways of improvement. Apart from reviewing existing studies on server power models and modeling methods, we further figure out several open challenges and possible research directions, such as the study on modeling the power consumption of lightweight virtual units like unikernel and the necessity of further explorations toward empowering server power estimation/prediction with machine learning. As power monitoring is drawing increasing attention from cloud service providers (CSPs), this survey provides useful guidelines on server power modeling and can be inspiring for further research on energy-efficient data centers.

中文翻译：

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云服务器的电源模型和电源建模的分类和调查

由于对云资源的需求不断增加，云数据中心数量和规模的不断增加，使得其海量的功耗成为当今的突出问题。有证据表明，云服务器的行为对数据中心的功耗产生了重大影响。尽管在这方面可以找到广泛的研究，但仍然缺少对云服务器整个层次结构（从底层硬件组件到上层应用程序）的模型和建模方法的系统回顾，这应该涵盖相关的研究物理和虚拟云服务器实例、服务器组件和云应用程序。在本文中，我们从三个角度总结了广泛的相关研究：电力数据采集、电力模型和云服务器（包括裸机、虚拟机 (VM) 和容器实例）。我们对服务器级功耗数据的收集方法、现有的从硬件到软件和应用程序的多个层次的主流功耗模型以及常用的功耗建模方法（包括经典回归分析和强化学习等新兴方法）进行了全面分类。在整个工作中，我们介绍了各种模型和方法，说明了它们的实现、可用性和适用性，同时讨论了现有方法的局限性和可能的​​改进方式。除了回顾关于服务器电源模型和建模方法的现有研究之外，我们还进一步找出了几个开放的挑战和可能的研究方向，例如对 unikernel 等轻量级虚拟单元的功耗建模的研究，以及进一步探索通过机器学习增强服务器功耗估计/预测的必要性。随着电源监控越来越受到云服务提供商 (CSP) 的关注，这项调查为服务器电源建模提供了有用的指导，并且可以启发对节能数据中心的进一步研究。