GREEN COMPUTING APPROACHES FOR FAULT-TOLERANT CLOUD NETWORK ARCHITECTURES: A SYNTHESIS OF SUSTAINABLE RESILIENCE STRATEGIES

Abstract

Abstract: The escalating energy demands of cloud data centers, projected to consume 20% of global electricity by 2025, pose a significant sustainability challenge. Simultaneously, ensuring high availability and fault tolerance in cloud networks is paramount for Service Level Agreement (SLA) compliance. This paper synthesizes contemporary research to demonstrate that green computing and fault tolerance are not competing but complementary objectives in cloud network architectures. We analyze and integrate advanced approaches—including AI-driven dynamic resource consolidation, renewable energy-aware scheduling, and biologically-inspired optimization algorithms—that collectively enhance both energy efficiency and system resilience. Our findings reveal that modern green computing strategies inherently contribute to fault tolerance by reducing thermal stress, minimizing aggressive migration-related network congestion, and enabling proactive failure prevention through predictive analytics. We propose a multi-layered architectural framework that leverages machine learning for workload prediction, deep reinforcement learning for adaptive VM placement, and federated management across geographically distributed data centers to balance renewable energy usage with reliability. The synthesis indicates that such integrated approaches can achieve energy savings of 25-35% while reducing network fault-induced SLA violations by 40-50%, establishing a clear pathway toward sustainable and resilient cloud infrastructures suitable for nextgeneration applications. Keywords: Green Computing, Fault Tolerance, Cloud Networks, Energy Efficiency, AI Optimization, Renewable Energy, SLA Compliance