Fault Tolerance and Resilience in REONs through STDP Mechanisms

Avadha Bihari, Ashutosh Kumar Singh, Chandan Chandan
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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
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通过 STDP 机制实现 REON 的容错性和复原力
光通信网络的快速发展需要创新方法来应对容错和网络弹性方面的挑战。本文的研究重点是通过将尖峰计时可塑性(STDP)机制(一种受生物启发的学习规则)与神经形态计算技术相结合,增强可重构弹性光网络(REONs)的容错性和弹性。研究强调了 REON 在动态重新分配资源和重新配置网络路径方面的灵活性,以管理不同的流量负载和突发故障。光网络中的传统故障管理方法通常依赖于预定义的备份路径,这种方法受限于延迟和次优性能。相比之下,STDP 提供了一种新颖的方法,允许网络通过不断学习过去的经验进行实时调整。这种自适应能力使 REON 更稳健、更高效,从而确保最大限度地减少停机时间,提高整体性能。研究得出结论,基于 STDP 的机制可以显著增强 REON 的适应性和容错性,使其非常适合动态和复杂的网络环境。未来的研究可以探索这些机制在大型网络中的可扩展性、与其他神经形态系统的集成以及在现实世界中的应用:REONs、STDP、容错、网络弹性、神经形态计算
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