EMO-TS: An Enhanced Multi-Objective Optimization Algorithm for Energy-Efficient Task Scheduling in Cloud Data Centers

The rapid expansion of cloud data centers, driven by the increasing demand for diverse user services, has escalated energy consumption and greenhouse gas emissions, posed severe environmental risks, and increased operational costs. Addressing these challenges requires innovative solutions for optimizing resource allocation without compromising service quality. This paper presents the Enhanced Multi-Objective Optimization Algorithm for Task Scheduling (EMO-TS). This novel approach integrates Deep Reinforcement Learning (DRL) and Enhanced Electric Fish Optimization (EEFO) to create an adaptive, dynamic, and energy-efficient scheduling framework. The primary objective of EMO-TS is to significantly reduce the energy consumption of cloud data centers while maintaining high levels of resource utilization, time efficiency, and service quality. Through the hybrid methodology of DRL and EEFO, EMO-TS dynamically adjusts task scheduling based on real-time workloads and operational conditions, effectively minimizing power consumption without sacrificing system performance. Additionally, EMO-TS introduces improvements in makespan and task execution, ensuring timely completion and optimal resource use. A comprehensive set of experiments and simulations demonstrates the practical implications of EMO-TS's results. EMO-TS outperforms traditional scheduling approaches, reducing energy consumption by up to 25% and decreasing makespan by 15%. These results underscore the algorithm's potential to address cloud service providers' economic and environmental concerns, offering a practical solution for green cloud computing initiatives. Furthermore, the integration of renewable energy sources within the EMO-TS framework shows potential for further reducing the carbon footprint of cloud operations, aligning with global sustainability goals.