Route Flap Early Prediction System and Method Based on Physical Layer Noise

Abstract

This invention relates to a route flap early prediction system and method based on physical layer noise. The system in question includes a physical telemetry acquisition module (1) which continuously collects physical layer data from network elements, a noise and trend analysis engine (2) which analyzes optical power fluctuations, FEC error rates and CRC trends by evaluating the data collected by the physical telemetry acquisition module (1), a link stability score calculator (3) which processes the results obtained by the noise and trend analysis engine (2) and produces a numerical risk value representing the stability level of the link, and a preventive action module (4) which intervenes when the risk level approaches critical thresholds and performs traffic routing, parameter optimization or maintenance alarm generation.

Description

1 TARIFF Route Flap Early Prediction System and Method Based on Physical Layer Noise Technical Area 5 The invention improves routing stability in telecommunications operator backbone and data center networks. route flap early prediction based on physical layer noise, used to improve It is related to the system and method. State of the Art Today, IP / MPLS operates with dynamic routing protocols such as BGP and IS-IS. Micro-disruptions occurring at the physical layer in infrastructures have not yet resulted in a connection interruption. It needs to be detected before the transformation. Existing systems usually have the route flap connected at link 15. after it crashes, that is, when events such as BGP neighbor reset or interface down occur It detects. This approach is reactive. Increased FEC errors at the physical layer increase optical power. Fluctuations or CRC trends are not associated with routing behavior. Consequently... The physical deterioration remains invisible for a long time and emerges as a sudden flare-up; this leads to loss of control. This leads to recalculations and temporary traffic disruptions on the plane. Basic technique 20 The problem is that physical layer degradation doesn't escalate into orientation instability. The unpredictability is the reason. Due to the negative aspects described above and the current solutions, the issue is... Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. Purpose of the Invention 25 The invention was created by drawing inspiration from existing situations and overcoming the aforementioned drawbacks. It aims to solve the problem. The main purpose of the invention is to analyze the noise and error signals obtained from the physical layer as a time series. By evaluating it through analysis, it generates a Link Stability Score and, based on this score, potential route flap options. The goal is to develop a system and method for predicting events in advance. Another objective of the invention is to investigate optical power variations, FEC correction rates, and CRC error trends. and by analyzing module temperature changes together with physical degradation and past routing 35 The goal is to create a risk model based on the correlation between uncertainties. 2 Another aim of the invention is to reduce traffic when the stability score falls below a specified threshold level. can gradually shift to alternative routes, temporarily changing routing parameters. The goal is to provide a system that can optimize or generate operational maintenance alarms; connectivity. Instead of a mechanism that reacts after a fall, it uses physical signals as an early warning source. By using it, it offers a proactive and deterministic layer of protection. 5 The structural and characteristic features and all the advantages of the invention are given in the figures below. Thanks to the detailed explanation written with references to the figures, it becomes clearer. It will be understood. Figures that will help understand the invention. Figure 1 shows the general architecture of the system that is the subject of the invention. Explanation of Part References 1. Physical telemetry collection module 15 2. Noise and trend analysis engine. 3. Link Stability Score Calculator 4. Preventive action module Detailed Description of the Invention 20 This detailed explanation describes the route flap early detection based on physical layer noise, which is the subject of the invention. The preferred structures for forecasting systems and methods are not only better understood in the context of the subject. It is explained in a way that facilitates understanding. This invention relates to a route flap early prediction system based on physical layer noise. The system described in this invention continuously processes physical layer data obtained from network elements. collecting physical telemetry collection module (1), physical telemetry collection module (1) By evaluating the data collected, optical power fluctuations, FEC error rates and 30 Noise and trend analysis engine (2) that analyzes CRC trends, noise and trend analysis Representing the stability level of the connection by processing the results obtained by the engine (2). The link stability score calculator (3) produces a numerical risk value, indicating the critical risk level. It intervenes when thresholds are approached, redirecting traffic and optimizing parameters. or preventive action module (4) 35 which performs maintenance alarm generation operations It includes. 3 Physical telemetry collection module (1), optical module DOM data, FEC counters, CRC errors It collects temperature information in real time and generates timestamped data. Noise and trend analysis engine (2), micro-optical power fluctuations and error increase trends It evaluates and identifies deviations using time series analysis. 5 The link stability score calculator (3) converts physical layer metrics into a single numerical risk value. It calculates the stability level of the connection by converting the data. The preventive action module (4) redirects traffic to alternative 10 when the stability score falls below the critical threshold. It shifts to different paths or optimizes routing parameters. In the method implemented with the system that is the subject of the invention; • Physical layer telemetry is continuously collected by the physical telemetry collection module (1). collected, 15 • The collected data is analyzed by the noise and trend analysis engine (2) to identify errors and deterioration trends are identified, • Using the analysis results, the link stability score is calculated by the link stability score calculator (3) The level of stability is calculated. • When the calculated stability score falls below the threshold value, the preventive action module (4) 20 Traffic redirection or optimization processes are implemented by [the relevant authority / organization]. The system described in the invention converts physical layer data obtained from network elements into physical layer data. It continuously collects data through the telemetry collection module (1). The collected data is free of noise and Optical power fluctuations are evaluated by the trend analysis engine (2), FEC error 25 The ratios and CRC trends are analyzed. The results obtained are used in the link stability score calculator. (3) processed by a numerical risk value representing the stability level of the connection. It is produced. If the risk level approaches critical thresholds, the preventive action module (4) by intervening to redirect traffic, optimize parameters, or generate maintenance alerts. It performs these operations. Thus, the system takes preventive action before the route flap occurs. 30 By providing this, it increases network continuity.

Claims

4 REQUESTS 1. It is a route flap early prediction system based on physical layer noise, and its feature is; • physical layer that continuously collects physical layer data from network elements telemetry collection module (1), 5 • optical power by evaluating the data collected by the physical telemetry acquisition module (1) Noise and trend analysis that analyzes fluctuations, FEC error rates, and CRC trends. engine (2), • by processing the results obtained by the noise and trend analysis engine (2) the connection Link stability score calculator 10, which generates a numerical risk value representing the level of stability. (3), • Traffic redirection by intervening when the risk level approaches critical thresholds, preventive measures that perform parameter optimization or generate maintenance alarms. action module (4) It includes. 15 2. It is a route flap early prediction method based on physical layer noise, and its characteristic is; • physical layer telemetry is continuously collected by the physical telemetry collection module (1) gathering, • The collected data is analyzed by the noise and trend analysis engine (2) and errors and 20 Identifying deterioration trends, • Using the analysis results, the link stability score is calculated by the link stability score calculator (3) Calculating the level of stability, • Preventive action module (4) when the calculated stability score falls below the threshold value 25 Implementation of traffic redirection or optimization processes by It includes the steps of the process.

Images