使用轻量级机制实现数据中心机架电源的完全分解

Q1 Computer Science IEEE Cloud Computing Pub Date : 2022-07-01 DOI:10.1109/CLOUD55607.2022.00052
Kalyan Dasgupta, Umamaheswari Devi, Aanchal Goyal
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引用次数: 0

摘要

由于对碳中和的日益关注以及遵守全球各国政府新出台的严格法规的要求,世界各地的企业越来越重视可持续性。随着许多企业工作负载部署在云和数据中心上,为了满足客户的强制性碳报告要求,云提供商和数据中心运营商不可避免地要量化每个客户在其设施的总碳排放中所占的份额。准确的碳量化要求在硬件基础设施(如物理服务器和网络交换机)的最低级别提供功率测量。然而,在许多数据中心中,功率传感非常有限,通常只能在聚合级别(如机架级别)进行测量。要深入到工作负载级别以捕获每个工作负载的正确电量使用情况,跨服务器分解这种电量是非常重要的。在本文中,我们提出了一个基于软件的非线性模型,使用牛顿-拉夫森方法来估计单个服务器的功率模型参数,当整个机架级功率测量给定时,使用服务器利用率。该方法适用于机架中有多种类型服务器的数据中心,并且是轻量级的,因为它不需要关闭单个服务器来估计空闲功率。该方法还可以推广到机架功率和服务器利用率测量的时间粒度可能不匹配的实际场景。我们对所提出的方法进行了详细的评估,并发现即使在多个不同的初始条件下进行测试,参数估计也具有良好的收敛性。
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Towards complete dis-aggregation of data center rack power using light-weight mechanisms
Enterprises world-wide are increasingly prioritizing sustainability due to the growing focus on carbon neutrality as well as the requirement to adhere to emerging strict regulations from governments across the globe. With many enterprise workloads deployed on cloud and data centers, to fulfill the mandatory carbon reporting requirements of their clients, it is becoming inevitable for cloud providers and data center operators to quantify each client’s share of the total carbon emission from their facility. Accurate carbon quantification requires power measurements to be available at the lowest level of the hardware infrastructure such as physical servers and network switches. However, power sensing is quite limited in many data centers, with measurements normally available only at an aggregated level such as the rack level. To drill down to the level of a workload to capture the correct power usage per workload, it is very important to dis-aggregate this power across servers. In this paper, we propose a software based non-linear model using the Newton-Raphson method to estimate the power model parameters of individual servers using server utilizations when the overall rack level power measurements are given. The methodology is applicable to data centers with multiple types of servers in a rack and is light-weight in the sense that it does not require mechanisms such as shutting down individual servers in order to estimate idle power. The method is also generalized to account for the real world scenario where the time granularity of rack power and server utilization measurements may not match. We have conducted detailed evaluations of the methods proposed and find good convergence for parameter estimation even when tested with multiple different initial conditions.
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来源期刊
IEEE Cloud Computing
IEEE Cloud Computing Computer Science-Computer Networks and Communications
CiteScore
11.20
自引率
0.00%
发文量
0
期刊介绍: Cessation. IEEE Cloud Computing is committed to the timely publication of peer-reviewed articles that provide innovative research ideas, applications results, and case studies in all areas of cloud computing. Topics relating to novel theory, algorithms, performance analyses and applications of techniques are covered. More specifically: Cloud software, Cloud security, Trade-offs between privacy and utility of cloud, Cloud in the business environment, Cloud economics, Cloud governance, Migrating to the cloud, Cloud standards, Development tools, Backup and recovery, Interoperability, Applications management, Data analytics, Communications protocols, Mobile cloud, Private clouds, Liability issues for data loss on clouds, Data integration, Big data, Cloud education, Cloud skill sets, Cloud energy consumption, The architecture of cloud computing, Applications in commerce, education, and industry, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Business Process as a Service (BPaaS)
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