Network Configuration Management Using ML Clustering and Forecasting
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Solution Overview
Problem
Current network planning processes are manual, time-consuming, and inefficient when scaling to handle tens of thousands of nodes, making it difficult to manage network growth and prioritize capital-intensive projects effectively, especially when considering long-term forecasting and risk factors.
Innovation Solution
A system and method for network configuration management that involves data collection, cleaning, clustering, forecasting, and optimization processes using fuzzy logic, algorithmic de-noising, and machine learning to determine necessary changes such as technology upgrades or node additions, based on data from network elements and external sources, to optimize network architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual network planning processes are used, then flexibility and control are maintained, but scalability and efficiency deteriorate when handling tens of thousands of nodes
Solution Approach 1:
The patent replaces manual mechanical network planning processes with automated computational systems. Machine learning models, optimization algorithms, and automated configuration tools substitute human planners, enabling the system to handle tens of thousands of nodes efficiently. This substitution transforms the planning process from a manual, iterative approach to an automated, scalable system that can process large datasets and generate configurations without proportional increases in human effort.
2Quantity of substance
If network nodes are scaled from tens of thousands to hundreds of thousands to meet growth demands, then network capacity increases, but manual planning becomes unmanageable and time-consuming
Solution Approach 1:
The patent implements preliminary action by pre-configuring network templates, policies, and parameters before scaling events occur. Machine learning models predict future network states and pre-generate configuration options, allowing the system to rapidly respond to scaling demands. When nodes are added from tens of thousands to hundreds of thousands, the system already has prepared configuration frameworks and optimization rules in place, eliminating the need for time-consuming manual planning during actual scaling events.
3Reliability
If capital-intensive network transformation projects are undertaken to support gigabit symmetrical speed offerings, then network capability improves, but project prioritization and resource allocation become more difficult
Solution Approach 1:
The patent implements feedback mechanisms where machine learning models continuously monitor network performance, utilization patterns, and service quality metrics. This feedback loop provides real-time information about which network transformations are delivering value and which areas need improvement. The system uses this feedback to dynamically adjust prioritization of capital-intensive projects, ensuring that resources are allocated to transformations that most improve network capability while maintaining manageable project portfolios through data-driven decision support.
Data Source
AI summary
Disclosed are systems and methods for network configuration management systems and methods. In some embodiments, the discloses systems and methods may involve receiving data from one or more nodes within a particular network. The data may include, for example, topology, telemetry, geographical, and other data relating to the nodes, the network, and/or the functionality of the nodes or network. Once received, such data may be cleaned and processing may be performed on the data. Such processing may involve clustering the data into groups based on various parameters and performing forecasting on the data to determine future usage rates and capacity information, for example. The clustering and forecasting results may be fed to a rules engine and/or an optimization engine, which may then determine appropriate actions to take on the network (e.g., changes to various nodes in terms of technology and/or number of nodes).