Computing server power modeling in a data center: survey,taxonomy and performance evaluation

TL;DR

This paper surveys and evaluates 24 software-based power models for data centers, providing a unified comparison across different architectures, benchmarking applications, and measurement techniques to identify the most effective approaches for energy efficiency.

Contribution

It introduces a comprehensive taxonomy and performs an objective evaluation of existing power models under a unified environment, considering hardware heterogeneity.

Findings

Unified evaluation framework for power models

Impact of hardware heterogeneity on model accuracy

Identification of best-performing power modeling approaches

Abstract

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 formula 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 technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper.