Intelligent Network Architecture Management via Machine Learning
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Solution Overview
Problem
Managing complex and distributed networks in large corporations or financial institutions is challenging due to varying configurations, security policies, and update schedules of network elements from different suppliers, making it difficult to maintain optimal performance and functionality.
Innovation Solution
A system and method for intelligent management of networked architectures using a processor to map, analyze, and reconfigure software and hardware elements, employing machine learning to identify necessary actions and mitigate negative impacts on network performance, including updates and patch management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual management methods are used for each network element, then configuration control is possible, but management efficiency deteriorates as network complexity increases
Solution Approach 1:
The system enables automatic self-service management through machine learning models that autonomously analyze network changes, predict conflicts, and implement reconfiguration actions without human intervention. The intelligent agent continuously learns from network data and automatically manages elements, allowing the system to serve itself rather than requiring manual management of each component.
Solution Approach 2:
The patent implements a universal intelligent management system that handles multiple types of network elements (software, hardware, virtual machines, containers) through a single multi-functional platform. The machine learning model serves multiple purposes: analyzing changes, predicting conflicts, determining reconfiguration actions, and evaluating performance impacts, replacing multiple specialized management tools with one universal system.
2Reliability
If updates and patches are applied to network elements, then security and functionality improve, but performance conflicts may occur
Solution Approach 1:
The system applies preliminary anti-action by using machine learning to predict potential performance conflicts before updates or patches are applied. The intelligent agent analyzes the proposed change, forecasts negative impacts on network performance, and prevents or prepares countermeasures against conflicts before they occur, rather than reacting after damage is done.
Solution Approach 2:
The patent implements preliminary action by automatically determining and testing reconfiguration actions before full deployment. The system evaluates potential changes in a simulated or controlled manner, identifies conflicts in advance, and prepares mitigation strategies before applying updates to the production network, ensuring reliability while preventing performance degradation.
3Extent of automation
If machine learning is used to determine reconfiguration actions, then automation improves, but system complexity increases
Solution Approach 1:
The patent uses an intelligent agent as an intermediary between raw network data and management decisions. This agent layer processes complex machine learning models and translates their outputs into actionable reconfiguration commands, simplifying the interface between automation and human operators while managing the complexity of the underlying AI systems.
Solution Approach 2:
The system implements continuous feedback loops where the machine learning model analyzes network performance data, learns from outcomes of previous reconfiguration actions, and adjusts its predictions and decisions accordingly. This feedback mechanism allows the system to improve automation effectiveness over time while managing complexity through iterative learning rather than requiring perfectly complex initial designs.
Data Source
AI summary
Intelligent learning and management of networked architectures is disclosed. A network architecture can be mapped to identify a set of interconnected hardware and software elements that comprise the network architecture. Data sources associated with the set of interconnected hardware and software elements can be identified and employed to compile data associated with the elements. The data can be utilized to determine an action to address potential negative effects of a change to the network architecture such as an update or patch. In one instance, the action corresponds to a reconfiguration of at least one of the set of interconnected hardware and software elements. Further, machine learning can be employed to determine a particular configuration. Once determined the action can be implemented on the network architecture.