TY - JOUR
T1 - Computing Server Power Modeling in a Data Center
T2 - Survey, Taxonomy, and Performance Evaluation
AU - Ismail, Leila
AU - Materwala, Huned
N1 - Funding Information:
This work is supported by the Emirates Center for Energy and Environment Research of the United Arab Emirates University under Grant 31R101. Authors’ addresses: L. Ismail (corresponding author.) and H. Materwala, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates; emails: {leila, huned.m}@uaeu.ac.ae. 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 permissions@acm.org. © 2020 Association for Computing Machinery. 0360-0300/2020/06-ART58 $15.00 https://doi.org/10.1145/3390605
Publisher Copyright:
© 2020 ACM.
PY - 2020/6
Y1 - 2020/6
N2 - Data centers are large-scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT), and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware-level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power-measurement techniques, and error-calculation formulas on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power-measurement techniques, and error formulas, with the aim of achieving an objective comparison. We use different server architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the article.
AB - Data centers are large-scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT), and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware-level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power-measurement techniques, and error-calculation formulas on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power-measurement techniques, and error formulas, with the aim of achieving an objective comparison. We use different server architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the article.
KW - Data center
KW - energy-efficiency
KW - green computing
KW - machine learning
KW - resource utilization
KW - server power consumption modeling
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U2 - 10.1145/3390605
DO - 10.1145/3390605
M3 - Article
AN - SCOPUS:85089413309
SN - 0360-0300
VL - 53
JO - ACM Computing Surveys
JF - ACM Computing Surveys
IS - 3
M1 - 58
ER -