Optimized Gated Fusion Adaptive Graph Neural Network Based Scheduling Framework for Efficient Resource Utilization in Cloud Computing

Cloud Computing (CC) becomes a fundamental platform for implementing large-scale and dynamic workflows; nevertheless, efficient resource utilization remains a critical challenge due to fluctuating workloads, energy constraints and heterogeneous virtual machine (VM) environments. To address these issues, an Optimized Gated Fusion Adaptive Graph Neural Network dependent Scheduling Framework in Cloud Computing for Efficient Resource Utilization (GFAGNN-SFER-CC-BKOA) is proposed. The framework integrates security, intelligent task clustering, adaptive resource monitoring, and meta-heuristic optimization to improve workflow scheduling performance. Initially, user authentication is ensured through a UUID-BLAKE based hashing mechanism, enabling secure access to cloud resources. Workflow tasks are then clustered using Localized Sparse Incomplete Multi-view Clustering (LSIMC) to reduce makespan and scheduling overhead. A Gated Fusion Adaptive Graph Neural Network (GFAGNN) is employed to monitor and predict VM resource availability by capturing dynamic spatio-temporal dependencies in cloud workloads. Finally, the Black-Winged Kite Optimization Algorithm (BKOA) selects optimal VMs for dynamic workflow scheduling, aiming to maximize resource utilization while decreasing energy consumption, cost and execution time. The proposed framework is evaluated using the GWA-T-12 Bitbrains dataset and implemented in Python. Experimental results demonstrate that GFAGNN-SFER-CC-BKOA significantly outperforms existing methods, including MOSF-A-RNN-CC, PMRP-DCRNN-CC and MOPWS-DQN-CC, achieving high accuracy of 99.7%. These outcomes confirm the efficacy of the GFAGNN-SFER-CC-BKOA in cloud computing environments for secure, adaptive and energy-efficient workflow scheduling.