Abstract

Purpose: Imagine a digital realm where every byte transmitted and every request processed incurs a cost beyond mere financial metrics, one measured in the energy essential for urban vitality and domestic illumination. The sprawling growth of cloud computing escalates its energy needs, prompting an urgent quest for a symbiosis of technological progress with environmental stewardship. This paper introduces a machine learning framework designed to deftly navigate the labyrinth of task scheduling algorithms, each promising efficiency but also demanding energy, thereby addressing the imperative for optimized resource utilization, reduced energy consumption, and advancement of green cloud computing. Methods: We propose a novel machine learning-based framework that evaluates and selects the most efficient task scheduling algorithms, including First-Come-First-Serve (FCFS), Shortest Job First (SJF), Round Robin (RR), and Particle Swarm Optimization (PSO). This framework emphasizes key performance metrics such as Total Energy Consumption and Average Energy Consumption per Task, in conjunction with throughput and makespan. Utilizing a simulated dataset generated via the CloudSim toolkit, which captures diverse task characteristics and performance metrics across various algorithms, we develop and train a predictive model. This model is designed to determine the optimal scheduling algorithm by analyzing real-time data with a particular focus on energy efficiency. Results: The implemented model has demonstrated its efficacy in minimizing energy consumption while simultaneously upholding high levels of service performance. These results underscore the model's capacity to select scheduling algorithms that not only perform efficiently in operational terms but also contribute effectively to the sustainability targets of green cloud computing. Conclusion: This research highlights the critical role of environmental considerations within cloud computing operations, emphasizing the potential of machine learning tools to forge a more sustainable path for computing practices. By showcasing a method that potentially lowers the energy footprint of cloud services without compromising service quality, the study offers a beacon of hope for aligning the expansion of cloud computing with the ecological imperatives of our times.