Authors:
K. Chitra, A. Chitra, G. Rajasekaran, Premanand Jothilingam, H. Mohamed Faizan Uwais, J. Mohamed Zakkariya Maricar
Addresses:
Department of Computer Applications, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India. Department of Computer Applications, St. Peter's Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Life Cycle Service, Yokogawa Corporation of America, West Valley City, Utah, United States of America. Department of Research and Development, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
Abstract:
Heterogeneity within the cloud infrastructure has also been among the factors responsible for the problem of effective resource management. Existing allocation algorithms are unable to balance the conflicting interests of cloud providers (revenue maximisation) and consumers (Quality of Service). Two game-theoretic auction mechanisms of resource management are introduced and compared in this paper: the traditional Nash Auction and an Energy-Aware Nash Auction. The primary objective is to compare their performance across resource utilisation, user satisfaction, provider revenues, and energy consumption. A simulated multi-cloud facility constructed with the CloudSim toolkit is used throughout the research. Synthetic data comprising 477 unique data samples, each creating user virtual machine requests along with physical machine specifications, was employed to commence the simulation. The Energy-Aware Nash Auction mechanism proposed adds an energy-consumption penalty to the provider's utility function to incentivise deployment on low-energy resources. Findings, derived from the Python, Pandas, and Matplotlib libraries, reveal that while the fundamental Nash Auction maximises providers' revenue, the Energy-Aware Nash Auction achieves substantial energy savings in total energy consumption with minimal effects on revenue and user satisfaction, and is a deployable model for green or eco-friendly cloud data centres.
Keywords: Cloud Computing; Resource Management; Heterogeneous Systems; Nash Auction; Game Theory; Energy Efficiency; Quality of Service; Nash Equilibrium; Economic Efficiency.
Received: 28/10/2024, Revised: 25/01/2025, Accepted: 21/03/2025, Published: 09/09/2025
DOI: 10.64091/ATICS.2025.000197
AVE Trends in Intelligent Computing Systems, 2025 Vol. 2 No. 3 , Pages: 133-141