A Hybrid Approach to Real-Time Fault Detection and Recovery in Federated Cloud Systems using Federated Byzantine Fault-Tolerant Cloud Recovery (FBFT-CR)
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Abstract
The increasing reliance on federated cloud environments for large-scale distributed applications has led to new challenges in ensuring fault tolerance and system availability. Traditional fault tolerance mechanisms often struggle to maintain system integrity in the face of diverse failures, including hardware malfunctions, network issues, and Byzantine faults. To address these challenges, we propose a novel Federated Byzantine Fault-Tolerant Cloud Recovery (FBFT-CR) framework that combines real-time fault detection, advanced recovery mechanisms, and Byzantine fault tolerance. The framework integrates dynamic machine learning-based fault prediction, hybrid recovery techniques such as checkpointing and replication, and the Byzantine Fault Tolerance (BFT) protocol to ensure system reliability in a federated cloud environment. The proposed approach provides a robust solution for ensuring high availability, minimizing downtime, and maintaining system correctness even in the presence of malicious or faulty nodes. Experimental results demonstrate the efficiency of FBFT-CR in mitigating system failures while maintaining system performance and scalability in a federated cloud infrastructure.
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