Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Author:

Sangeetha Sivakumar1,Logeshwaran Jaganathan2,Faheem Muhammad3ORCID,Kannadasan Raju4ORCID,Sundararaju Suganthi5,Vijayaraja Loganathan6

Affiliation:

1. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

2. Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

3. Department of Computing Science, School of Technology and Innovations University of Vaasa Vaasa Finland

4. Department of Electrical and Electronics Engineering Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu India

5. Department of Artificial Intelligence Sri Sairam Institute Technology Chennai Tamil Nadu India

6. Department of Electrical and Electronics Engineering Sri Sairam Institute Technology Chennai Tamil Nadu India

Abstract

AbstractThis paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

Publisher

Institution of Engineering and Technology (IET)

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