Energy-Aware AI-based Optimal Cloud Infra Allocation for Provisioning of Resources
Okechukwu Clement Agomuo, Osei Wusu Brempong, Junaid Hussain Muzamal
Abstract
This paper presents a comprehensive study on optimizing resource allocation in cloud computing environments using an ensemble of machine learning techniques and optimization algorithms. We developed a multifaceted approach, integrating Long Short-Term Memory (LSTM) networks for forecasting resource demands, Particle Swarm Optimization (PSO) for initial resource allocation, Q-learning for dynamic resource adjustment, and Linear Regression (LR) for predicting energy consumption. Our LSTM model demonstrated high accuracy in demand forecasting, with detailed performance metrics indicating its effectiveness in diverse scenarios. The PSO algorithm significantly enhanced the efficiency of resource distribution, evidenced by a reduction in the number of utilized units. Q-learning contributed to the system’s adaptability, optimizing resource allocation based on changing demands in real-time. The LR model accurately predicted energy consumption, aligning closely with observed data and highlighting the potential for energy-efficient cloud management