Innovations and Challenges in Green Cloud Computing Via Leed, Ant Colony Optimization

Abstract

This paper investigates how Green Cloud Computing may be made more efficient by combining LEED standards with Ant Colony Optimization (ACO), which is an approach to cloud computing that takes environmental considerations into account. The goal is to maximize resource allocation in data centers while minimizing energy use and carbon impact. The innovative combination of ACO and LEED offers a fresh perspective on achieving a balance between computational performance and environmental sustainability. The goal is to design a system that uses ACO-driven algorithms to optimize energy consumption in cloud infrastructure so it meets LEED standards. Innovative solutions for large-scale cloud settings are needed to address the challenges of scalability and convergence speed that ACO faces. Further, in order to widen the scope, Multi-Objective Optimization (MOO) and Particle Swarm Optimization (PSO) are investigated. While MOO has it tough dealing with competing goals, PSO has trouble striking a balance between the two. We can learn more about how to optimize cloud operations while reducing environmental effect thanks to innovations in these fields. By solving problems in ACO, PSO, and MOO, this study paves the way for Green Cloud Computing to become more efficient and less harmful to the environment. © 2023 IEEE.