Machine Learning Auto-Correction for Dynamic Distributed Networks
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Solution Overview
Problem
Current technologies are inadequate in automatically detecting and solving dynamic issues in distributed networks without human administrator intervention, leading to inefficiencies and potential application failures.
Innovation Solution
A system utilizing machine learning and neural networks to automatically detect, analyze, and deploy executable operations to resolve dynamic issues in distributed networks, including memory utilization, data accessibility, and application configuration problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional technologies are used to monitor and manage distributed network issues, then administrator intervention is required to detect and solve dynamic issues, but this leads to delayed response times and potential application failures
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network nodes and analyzing application data in real-time to detect issues before they cause application failures. The machine learning models are pre-trained on historical data to identify patterns and predict potential problems, enabling proactive intervention rather than reactive response.
Solution Approach 2:
The system implements self-service by automatically detecting, analyzing, and resolving dynamic network issues without requiring administrator intervention. The autonomous agent uses machine learning models to diagnose problems and execute corrective operations, enabling the system to manage itself and eliminate the time loss associated with human response.
2Ease of operation
If manual monitoring and intervention methods are used, then administrators can solve issues, but the complexity of managing thousands of network nodes increases operational burden
Solution Approach 1:
The autonomous agent enables self-service by automatically monitoring, detecting, and resolving issues across thousands of network nodes without administrator intervention. This eliminates the operational burden of manual management while scaling to handle large distributed networks, as the system manages itself autonomously.
Solution Approach 2:
The system implements universality through a multi-functional autonomous agent that can detect various types of issues (memory problems, data accessibility issues, configuration errors), analyze them using multiple machine learning models, and execute diverse corrective operations across different network nodes and applications, providing comprehensive management capability.
3Productivity
If real-time automatic detection and solution deployment is implemented, then response time is reduced and reliability is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The system applies segmentation by dividing the complex task of issue resolution into distinct functional modules: data collection, anomaly detection, pattern recognition, solution generation, and execution. Each module is handled by specialized machine learning models or algorithms, making the overall system more manageable and efficient despite the high productivity requirements.
Data Source
AI summary
A system for implementing auto-correction to solve dynamic issues in a distributed network comprises a processor associated with a server. The processor detects an application issue at a network node. The application issue comprises a user request with an issue statement and a user interaction associated with one or more operation parameters of an application. The processor receives a set of data objects associated with the application issue. The processor classifies the data objects into one or more issue patterns by a machine learning model. The processor processes the one or more issue patterns and application information through a neural network to determine executable operations configured to solve the application issue. In response to determining that the application is running at the network node, the processor deploys the executable operations to the network node to correct the one or more parameters of the application to prevent a failure application.