PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing

Leila Ismail, Huned Materwala

Research output: Contribution to journalConference articlepeer-review

Abstract

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

Original languageEnglish
Pages (from-to)385-395
Number of pages11
JournalProcedia Computer Science
Volume238
DOIs
Publication statusPublished - 2024
Event15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024 - Hasselt, Belgium
Duration: Apr 23 2024Apr 25 2024

Keywords

  • cloud computing
  • dataset generation
  • edge computing
  • energy-efficiency
  • green computing machine learning
  • internet of things
  • power consumption modeling
  • resource management
  • scheduling

ASJC Scopus subject areas

  • General Computer Science

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