Pro-active Performance Monitoring in Optical Networks using Frequency Aware Seq2Seq Model

Rishabh Jain, Umesh Sajjanar
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Abstract

Performance Monitoring (PM) and Fault Detection have always been a reactionary approach in Optical Networks for most service providers. Any kind of fault (power surge, ageing issues, equipment faults and failures, natural calamities, etc.) in an optical network is detected only after the fault has occurred and mitigation is performed afterward. The resultant service outages for end-users cause huge financial and reputation losses to the vendors. Therefore, there is a strong need for proactive detection of faults to limit disruption and provide uninterrupted services to clients. We achieve this objective by doing a multi-horizon time series prediction of Bit Error Rate at the receiver end of an optical circuit using our custom designed Frequency aware Sequence to Sequence (FaS2S) Neural Network. The predicted value of BER can be used to notify users of failure scenarios before they occur. Further corrective action, such as automatic re-routing or manual intervention can then be taken by the user. With this model, we can even configure the network properties dynamically during periods of low BER to push the network efficiency to its maximum capacity. See inference Video for BER inference capabilities of FaS2S.
基于频率感知Seq2Seq模型的光网络主动性能监测
对于大多数服务提供商来说,性能监控和故障检测一直是光网络中的一种反动方法。光网络中的任何类型的故障(电涌、老化问题、设备故障和故障、自然灾害等)只有在故障发生后才会被检测到,并在故障发生后进行缓解。最终用户的服务中断会给供应商带来巨大的财务和声誉损失。因此,迫切需要主动检测故障,以限制中断并为客户提供不间断的服务。我们通过使用自定义设计的频率感知序列到序列(FaS2S)神经网络在光学电路的接收端进行误码率的多水平时间序列预测来实现这一目标。误码率的预测值可用于在故障场景发生之前通知用户。然后,用户可以采取进一步的纠正措施,例如自动重新布线或人工干预。利用该模型,我们甚至可以在低误码率期间动态配置网络属性,以使网络效率达到最大容量。FaS2S的误码率推断能力见推理视频。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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