Generative AI Framework for Network Telemetry Analysis
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Solution Overview
Problem
Traditional analytic approaches are inadequate for efficiently processing and analyzing the vast volume and high velocity of network telemetry data, hindering network operators' ability to detect patterns, anomalies, and potential issues in a timely manner.
Innovation Solution
A generative AI architecture leveraging Large Language Models (LLMs) and cloud native infrastructure to process, analyze, and derive insights from long-term network telemetry data, utilizing data streaming producers, serverless compute, and ETL jobs to prepare data for LLM inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional analytic approaches are used to process network telemetry data, then the system complexity remains low, but the productivity and speed of pattern and anomaly detection deteriorate due to the enormous volume and high velocity of data
Solution Approach 1:
The system segments the data processing workflow into distinct functional modules: data ingestion layer (Kafka), transformation layer (Spark/Flink), storage layer (Data Lake), and analysis layer (LLM). This segmentation allows each component to handle specific aspects of data processing independently, improving overall productivity while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components such as Kafka as a message queue system and Spark/Flink as transformation engines that mediate between data sources and the LLM analysis layer. These intermediaries buffer, transform, and prepare data in manageable batches, enabling high-velocity data to be processed efficiently without directly overwhelming the analysis system.
2Measurement precision
If network telemetry data is stored and analyzed over longer periods, then the ability to identify trends and patterns improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data transformation, cleaning, and feature extraction using Spark/Flink before data is sent to the LLM. This preliminary action prepares the data in advance, reducing the processing time required during actual analysis while maintaining the ability to identify trends in long-term data.
Solution Approach 2:
The architecture implements periodic batch processing where data is ingested, transformed, and analyzed in scheduled batches rather than continuously. This allows the system to process long-term data in manageable cycles, improving measurement precision while controlling processing time through controlled batch sizes and frequencies.
3Speed
If data is processed in real-time through streaming, then the speed of insight generation improves, but the reliability and fault tolerance of the system deteriorate due to the high velocity and volume of data
Solution Approach 1:
The system implements fault tolerance mechanisms in advance by using Kafka's built-in fault tolerance for data streaming and Spark/Flink's fault recovery capabilities for data transformation. These systems are designed to handle data loss, system failures, and processing errors before they impact the overall system reliability, cushioning against potential failures while maintaining real-time processing speed.
Data Source
AI summary
In some implementations, the device may include initiating a stream producer that sends formatted data to a topic. In addition, the device may include ingesting, by a sink connector, the formatted data into an object storage service. The device may include implementing an event-driven serverless compute, where the event-driven serverless compute is triggered automatically when any new data is ingested to the object storage service, and where the event-driven serverless compute reads the JSON data, converts it to transformed data, and writes the transformed data to a distributed data store. Moreover, the device may include creating an ETL job, where the ETL job reads the data, further transforms the data, and writes it back into the distributed data store as ETL transformed data. Also, the device may include sending the ETL transformed data to an LLM API in batches to create inference results, where the batches are queued to manage the rate limits. Further, the device may include storing the inference results in cache storage. In addition, the device may include implementing an API gateway for secure access to inference results.