A Graph Neural Network-Based Approach With Dynamic Multiqueue Optimization Scheduling (DMQOS) for Efficient Fault Tolerance and Load Balancing in Cloud Computing

Abstract

Currently, cloud computing is increasing on a daily basis and has evolved into an efficient and flexible paradigm for addressing large-scale issues. It is recognized as an internet-based computing model where various cloud users share computing and virtual resources such as services, applications, storage, servers and networks. In the present study, we propose an innovative strategy for enhancing the fault tolerance and load balancing capabilities of cloud computing environments: we combined graph neural networks (GNNs) with dynamic multiqueue optimization scheduling (DMQOS). The present study uses GNNs and DMQOS to provide a novel solution to these challenges. GNN–DMQS uses a DMQOS system that adjusts to the dynamic nature of cloud workloads. This dynamic method develops response times and resource consumption, which improve load balancing and system effectiveness. Using GNNs to predict and mitigate probable faults grows fault tolerance and safeguards service accessibility. We evaluate the proposed method, GNN–DMQOS, using extensive experiments on real-world cloud computing datasets. The results demonstrate significant developments: 95.66% in fault tolerance, 97.13% in adaptability, 1598.14 kbps in throughput, 94.78% in resource utilization, 96.77% in reliability, 2.876 ms in response time, 0.141 s in network lifetime, 1.627 s in end-to-end delay and 129.34 ms in time complexity compared with traditional methods. In addition, our method, GNN–DMQOS, exhibits adaptability to varying workloads, making it suitable for dynamic cloud environments.