Usage centric green performance indicators

Author:

Chen Doron1,Henis Ealan1,Kat Ronen I.1,Sotnikov Dmitry1,Cappiello Cinzia2,Ferreira Alexandre Mello2,Pernici Barbara2,Vitali Monica2,Jiang Tao3,Liu Jia3,Kipp Alexander3

Affiliation:

1. IBM Research, Haifa University Campus, Mount Carmel, Haifa, Israel

2. Politecnico di Milano, Milan, Italy

3. High Performance Computing, Center Stuttgart, Stuttgart, Germany

Abstract

Energy effciency of data centers is gaining importance as energy consumption and carbon footprint awareness are rising. Green Performance Indicators (GPIs) provide measurable means to assess the energy effciency of a resource or system. Most of the metrics commonly used today measure the energy effciency potential of a resource, system or application usage, rather than the energy effciency of the actual usage. In this paper, we argue that the way that the resources and systems are actually used in a given data center configuration is at least as important as the effciency potential of the raw resources or systems. Hence, for data center energy effciency, we suggest to both select energy effcient components (as done today), as well as optimize the actual usage of the components and systems in the data center. To achieve the latter, optimization of usage centric GPI metrics should be employed and targeted as a primary green goal. In this paper we identify and present usage centric metrics, which should be monitored and optimized for improving energy effciency, and hence, reduce the data center carbon footprint.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference16 articles.

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1. Sustainable Transformation of Romanian Companies through Industry 4.0, Green Production and Environment Commitment;www.amfiteatrueconomic.ro;2022-02

2. A Study on the Relationship between Usability of GUIs and Power Consumption of a PC: The Case of PHRs;International Journal of Environmental Research and Public Health;2021-02-03

3. Quantifying and Optimizing Data Access Parallelism on Manycores;2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS);2018-09

4. EEUI: a new measure to monitor and manage energy efficiency in data centers;International Journal of Productivity and Performance Management;2018-01-08

5. Metrics for Sustainable Data Centers;IEEE Transactions on Sustainable Computing;2017-07-01

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