Fault Tolerance and Resilience in REONs through STDP Mechanisms

Abstract

The rapid advancement of optical communication networks necessitates innovative approaches to address challenges in fault tolerance and network resilience. Here focuses on enhancing the fault tolerance and resilience of Reconfigurable Elastic Optical Networks (REONs) by integrating Spike-Timing-Dependent Plasticity (STDP) mechanisms, a biologically inspired learning rule, with neuromorphic computing techniques. The research highlights the flexibility of REONs in dynamically reallocating resources and reconfiguring network paths to manage varying traffic loads and unexpected faults. The traditional fault management methods in optical networks, which often rely on predefined backup paths, are limited by delays and suboptimal performance. By contrast, STDP offers a novel approach that allows the network to adapt in real-time through continuous learning from past experiences. This adaptive capability makes REONs more robust and efficient, ensuring minimized downtime and improved overall performance. The study concludes that STDP-based mechanisms can significantly enhance the adaptability and fault tolerance of REONs, making them well-suited for dynamic and complex network environments. Future research could explore the scalability of these mechanisms in larger networks, their integration with other neuromorphic systems, and their application in real-world scenarios Key Words: REONs, STDP, Fault tolerance, Network resilience, Neuromorphic computing