Computing Server Power Modeling in a Data Center: Survey, Taxonomy, and Performance Evaluation

Leila Ismail, Huned Materwala

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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.

Original languageEnglish
Article number58
JournalACM Computing Surveys
Issue number3
Publication statusPublished - Jun 2020


  • Data center
  • energy-efficiency
  • green computing
  • machine learning
  • resource utilization
  • server power consumption modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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