AI-Powered Data Center Risk Prediction System Based on Vibration Behavior in Fiber Optic Infrastructure

Abstract

This invention relates to an AI-powered data center risk prediction system based on the vibration behavior of fiber optic infrastructure. By continuously monitoring the physical vibration behavior of the fiber optic line, the system detects disruptions at the physical layer before they occur, thereby improving data center connectivity and network operational stability.

Description

1 TARIFF AI-Powered Data Center Based on Vibration Behavior in Fiber Optic Infrastructure Risk Prediction System Technical Area This invention bridges the gap between telecommunications operator backbones and geographically distributed data centers. used to verify the true physical independence of the fiber optic connections provided, By analyzing the physical behavior of fiber optic paths, 10 logically different paths can be identified. AI-powered system that detects whether or not they actually share the same physical infrastructure Thanks to the analysis layer, path signatures are determined not by static rules, but by learned physical behavior. evaluating through models, especially active-active data centers and high availability. Artificial intelligence based on vibration behavior in fiber optic infrastructure designed for infrastructures requiring it. It relates to an intelligent data center risk prediction system. 15 State of the Art Nowadays, physical degradation on fiber optic cables usually occurs after a connection interruption or fault. It is detected after the rate increases. Distributed acoustic sensing systems detect vibration events 20 Although they can detect it, these systems do not predict the possibility of network outages for a specific time period, and It does not work integrated with network management systems. Therefore, the physical risk is only a malfunction. It becomes visible when it occurs. The fundamental technical problem is physical vibration in the fiber optic cable. the possibility of disruptions that its behavior could cause to data center connections in advance and The inability to predict automatically. The 25 mentioned that cannot be solved with current technology. The problems have made it necessary to make an innovation in the relevant technical field. Purpose of the Invention The present invention aims to eliminate the aforementioned disadvantages and introduce 30 new technologies to the relevant technical field. AI-powered systems based on vibration behavior in fiber optic infrastructure to bring advantages. It is related to data center risk assessment systems. The main purpose of the invention is to continuously monitor the physical vibration behavior of the fiber optic cable, thereby determining its physical characteristics. By detecting disruptions at the layer before they turn into interruptions, the data center 35 The goal is to increase the continuity of connections and the operational stability of the network. 2 Another aim of the invention is to analyze microphase changes, delay jitter variance, and feedback in fiber optic cables. By continuously monitoring physical parameters such as scattering characteristics, the data obtained by processing it into a multidimensional stress vector that represents the physical state of the fiber optic cable. transforming the generated stress vector from past failure patterns and learned behavior Calculating the probability of disruption by comparing it with the models, the calculated risk score is determined as 5 Traffic redirection, backup capacity activation, or alarm generation when the threshold value is exceeded. The goal is to provide preventative action before a disruption occurs by carrying out these procedures. To achieve the purposes described above, the invention utilizes physical vibrations in the fiber optic cable. The fiber vibration measurement module, which continuously collects data, displays the collected vibration data in 10 a physical stress vector generation engine that processes and generates a physical stress vector, is created AI-based interrupt system calculates the probability of interruption by analyzing the physical stress vector. The risk prediction model indicates that traffic will decrease if the calculated risk score exceeds a threshold value. proactive measures include routing, backup capacity activation, or alarm generation. It includes a network action module. 15 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. This will be understood, and therefore the evaluation should also take these figures and detailed explanations into account. It must be done by taking 20. Figures that will help understand the invention. Figure 1 shows the general architecture of the system that is the subject of the invention. Description of Part References 25 1. Fiber vibration measurement module 2. Physical stress vector generation engine. 3. AI-based outage risk prediction model 4. Proactive network action module Detailed Description of the Invention This detailed explanation is based on the vibration behavior of the fiber optic infrastructure that is the subject of the invention. AI-powered data center risk prediction systems are the preferred configurations, not just those mentioned above. This is explained to facilitate a better understanding of the subject. 35 3 This invention enables AI-powered data centers based on vibration behavior in fiber optic infrastructure. This is related to the risk prediction system. Accordingly, the system uses physical vibration data in the fiber optic line. The fiber vibration measurement module (1) continuously collects and processes the collected vibration data. The physical stress vector generating engine (2), which creates the physical stress vector, the physical stress vector created AI-based outage risk assessment 5 calculates the probability of an outage by analyzing the stress vector. The prediction model (3) states that traffic will increase if the calculated risk score exceeds the threshold value. proactive measures include routing, backup capacity activation, or alarm generation. It includes network action module (4). The system measures the physical vibration behavior of the fiber line through the fiber vibration measurement module (1) 10 It monitors and collects continuously. The data obtained is used to generate physical stress vectors (2) a stress vector that represents the physical condition of the fiber optic cable, analyzed by is created. The stress vector created is used in the AI-based disruption risk prediction model (3) interruption by comparing it with past failure patterns and learned behavior models It enables the generation of a risk score representing the probability. The predetermined threshold value of the risk score is 15. If it exceeds the limit, the proactive network action module (4) will be activated to redirect traffic and provide backup. It performs capacity activation or alarm generation operations. Thus, the system activates the physical capacity. by detecting disruptions at the data center before they turn into interruptions. It increases the continuity of connections and the operational stability of the network. In the system, the fiber vibration measurement module (1) measures the microphase and delay variance on the fiber. It measures the physical stress vector of the motor (2) vibration data into a multidimensional signature. transforms. AI-based outage risk prediction model (3) for outage for specific time interval. It calculates the probability. The proactive network action module (4) directs traffic according to the risk score and It generates an alarm. 25 The steps involved in the system's operation are as follows: • Physical vibration data in the fiber line is continuously collected by the fiber vibration measurement module (1) It is collected as follows. • The collected vibration data is processed by the physical stress vector generation engine (2) 30 A physical stress vector is created. • The generated physical stress vector is used in the AI-based disruption risk prediction model (3) The probability of a disruption is calculated by analyzing the data. • Proactive network action module (4) if the calculated risk score exceeds the threshold value Traffic routing, backup capacity activation, or alarm generation processes by 35 It is applied.

Claims

4 REQUESTS 1. AI-powered data center risk assessment based on vibration behavior in fiber optic infrastructure. It is a prediction system; its feature is: • Fiber vibration measurement that continuously collects physical vibration data in the fiber optic line. module (1), 5 • Physical stress vectors are created by processing the collected vibration data. vector generation engine (2), • Calculates the probability of failure by analyzing the generated physical stress vector. AI-based disruption risk prediction model (3), • If the calculated risk score exceeds the threshold value, traffic will be redirected to a backup plan. Proactive network actions that implement capacity activation or alarm generation processes. module (4) It includes.

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