DL-GAA: A Closed-Loop Hybrid Deep Learning and Green Anaconda Algorithm Framework for Intelligent Cloud Load Balancing

Abstract Efficient resource management in cloud data centers remains a critical challenge due to highly dynamic workloads, heterogeneous resources, and strict Service Level Agreement (SLA) requirements. Unstable resource allocation and improper virtual machine (VM) consolidation may lead to increased SLA violations, excessive energy consumption, and frequent VM migrations. Many existing approaches rely on reactive decision-making based solely on the current system state, while others treat workload prediction and optimization as separate processes, limiting the potential synergy between them. To address these limitations, this paper proposes DL-GAA, an intelligent framework for dynamic load balancing and VM consolidation in cloud environments. The proposed approach integrates Long Short-Term Memory (LSTM)–based workload prediction with the Green Anaconda Algorithm (GAA) within a unified closed-loop architecture. In this framework, predicted future workload trends guide the optimization process, while optimization outcomes provide feedback to improve subsequent decision-making. The performance of the proposed framework is evaluated through simulation using CloudSim 5.0 based on two real-world workload traces: PlanetLab and Azure 2019. Experimental results demonstrate that under the PlanetLab workload, DL-GAA achieves average reductions of 19.5% in SLA violations, 18% in energy consumption, and 23% in VM migrations. Similarly, under the Azure 2019 workload, the proposed approach reduces SLA violations by 24.3%, energy consumption by 12.3%, and VM migrations by 18.5% on average. These results indicate that combining deep learning–based workload forecasting with predictive metaheuristic optimization can significantly enhance service reliability, energy efficiency, and operational stability in cloud infrastructures, positioning DL-GAA as an effective solution for intelligent resource management in modern cloud data centers.