Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing

Efficient resource allocation in cloud computing is critical for optimizing execution time, minimizing delays, and improving system reliability. Traditional heuristic-based scheduling approaches struggle to adapt to dynamic workloads and heterogeneous virtual machines (VMs), leading to suboptimal performance. This paper proposes a hybrid scheduling framework that integrates Recurrent Neural Networks (RNNs) for execution time prediction and Decision Trees (DTs) for VM classification, enhancing resource allocation efficiency. The RNN model uses historical execution data to accurately predict task execution time, while the DT model classifies VMs based on performance characteristics, ensuring optimal task-to- VM assignments. The proposed method dynamically adapts to workload variations, reducing execution delays and improving Quality of Service (QoS) metrics. Experimental evaluations demonstrate that the hybrid RNN-DT approach outperforms traditional scheduling methods and metaheuristic algorithms, such as Genetic Algorithm and Artificial Bee Colony, in terms of execution time reduction, reliability, and delay minimization.