Deep Learning Enhanced Energy-Aware Task Scheduling for Efficient Cloud Datacenter Management

Abstract

Energy efficiency in cloud computing has become a critical concern due to the growing energy demands of large-scale datacenters, operational costs and environmental impacts. This research proposes Deep Learning-Enhanced Energy-Aware Task Scheduling (DL-EATS), a novel solution that combines LSTM-based workload prediction with a hybrid multi-objective optimization model integrating Genetic Algorithm and Particle Swarm refinement. DL-EATS intelligently schedules tasks to virtual machines or containers by anticipating workload variations, thereby minimizing energy consumption, ensuring SLA compliance and maximizing system throughput. The method was evaluated against state-of-the-art algorithms, including Reinforcement Learning-based Virtual Machine Placement (RLVMP), Enhanced Adaptive Moth-Flame Optimization (EA-MFO), Q-Learning-based Resource Dynamic Optimization (Q-RDO) and Task Scheduling using Grey Wolf Optimizer (TS-GWO), under identical cloud workload scenarios. Experimental results demonstrate that DL-EATS achieves the lowest energy consumption (480 kWh), shortest makespan (38 s), minimal SLA violation rate (1.2%) and highest resource utilization (92%), representing an 18.5% improvement in energy efficiency over the next best method and substantial gains across all performance metrics. These findings confirm that integrating predictive deep learning with hybrid heuristic optimization provides a scalable, reliable and energy-efficient solution for modern cloud datacenter management.