Enhanced virtual machine migration for energy sustainability optimization in cloud computing through knowledge acquisition

  1. Seddiki, Doraid 1
  2. Maldonado Carrascosa, Francisco Javier 1
  3. García Galán, Sebastián 1
  4. Valverde Ibáñez, Manuel 1
  5. Marciniak, Tomasz 2
  6. Ruiz Reyes, Nicolás 1
  1. 1 Telecommunications Engineering Department, Linares Higher Polytechnic School, Jaén University, Avenida de la Universidad S/N, 23700, Linares, Jaén, Spain
  2. 2 Institute of Telecommunications and Computer Sciences, Bydgoszcz University of Science and Technology, Profesora Sylwestra Kaliskiego 7, 85-796 Bydgoszcz, Poland
Revista:
Computers and Electrical Engineering

ISSN: 0045-7906

Año de publicación: 2024

Volumen: 119

Número: A

Páginas: 109506

Tipo: Artículo

DOI: 10.1016/J.COMPELECENG.2024.109506 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Computers and Electrical Engineering

Resumen

Cloud computing has revolutionized the way businesses and organizations manage their computational workloads. However, the massive data centers that support cloud services consume a lot of energy, making energy sustainability a critical concern. To address this challenge, this article introduces an innovative approach to optimize energy consumption in cloud computing environments through knowledge acquisition. The proposed method uses a Knowledge Acquisition version of the Gray Wolf Optimizer (KAGWO) algorithm to collect data on the availability and use of renewable energy within data centers, contributing to improved energy sustainability in cloud computing. The proposed KAGWO method is introduced to provide a systematic approach for addressing complex problems by integrating knowledge and global optimization principles, enhancing decision-making processes with fewer configuration parameters. This article conducts a comparative analysis between the KAGWO algorithm and the Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) and a Genetic Algorithm (Pittsburgh) to highlight the benefits and advantages of the former. By comparing the performance of KAGWO, Pittsburgh and KASIA in terms of energy sustainability, the study offers valuable insights into the effectiveness of knowledge-acquisition-based algorithms for optimizing renewable energy usage in cloud computing environments. The results demonstrate that the KAGWO algorithm outperforms KASIA and Pittsburgh by offering more accurate and data acquisition capabilities, resulting in enhanced energy sustainability. Overall, this study demonstrates substantial KAGWO performance improvements ranging from 0.53% to 5.23% over previous paper baselines, with particular significance found in slightly outperforming KASIA and Pittsburgh new results in small, medium and large scenarios.

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