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10.1145/1807128.1807136acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
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Virtual machine power metering and provisioning

Published: 10 June 2010 Publication History

Abstract

Virtualization is often used in cloud computing platforms for its several advantages in efficiently managing resources. However, virtualization raises certain additional challenges, and one of them is lack of power metering for virtual machines (VMs). Power management requirements in modern data centers have led to most new servers providing power usage measurement in hardware and alternate solutions exist for older servers using circuit and outlet level measurements. However, VM power cannot be measured purely in hardware. We present a solution for VM power metering, named Joulemeter. We build power models to infer power consumption from resource usage at runtime and identify the challenges that arise when applying such models for VM power metering. We show how existing instrumentation in server hardware and hypervisors can be used to build the required power models on real platforms with low error. Our approach is designed to operate with extremely low runtime overhead while providing practically useful accuracy. We illustrate the use of the proposed metering capability for VM power capping, a technique to reduce power provisioning costs in data centers. Experiments are performed on server traces from several thousand production servers, hosting Microsoft's real-world applications such as Windows Live Messenger. The results show that not only does VM power metering allows virtualized data centers to achieve the same savings that non-virtualized data centers achieved through physical server power capping, but also that it enables further savings in provisioning costs with virtualization.

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cover image ACM Conferences
SoCC '10: Proceedings of the 1st ACM symposium on Cloud computing
June 2010
264 pages
ISBN:9781450300360
DOI:10.1145/1807128
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 10 June 2010

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Author Tags

  1. datacenter power management
  2. power capping
  3. virtualization

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Overall Acceptance Rate 169 of 722 submissions, 23%

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  • (2024)Empirical Evaluation of ML Models for Per-Job Power PredictionCompanion of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629527.3651418(181-188)Online publication date: 7-May-2024
  • (2024)A Protocol to Assess the Accuracy of Process-Level Power Models2024 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER59578.2024.00014(74-84)Online publication date: 24-Sep-2024
  • (2024)Process-Based Efficient Power Level Exporter2024 IEEE 17th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD62652.2024.00058(456-467)Online publication date: 7-Jul-2024
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  • (2023)Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data CentersAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0801018:1(1-11)Online publication date: Jan-2023
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