ML Resource Planning Framework for Enterprise Wireless Networks
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Solution Overview
Problem
Current resource planning in enterprise wireless networks is complex and requires manual intervention, lacking scalable and accurate analytics to optimize resource allocation and network performance.
Innovation Solution
A scalable machine-learning (ML)-based framework for resource planning that identifies resource-constrained elements, determines contributing factors, and generates recommendations to address resource issues, utilizing both real-time monitoring and batch analysis phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for resource planning, then skilled personnel can make decisions about resource deployment, but the process becomes complex and time-consuming
Solution Approach 1:
The system enables automated resource planning where the network elements and resource management system perform the planning functions autonomously using machine learning models, eliminating the need for manual skilled personnel intervention while maintaining accurate resource deployment decisions
Solution Approach 2:
The patent replaces manual mechanical processes (skilled personnel making decisions) with an automated machine learning-based system that analyzes network data and generates resource planning recommendations, significantly reducing planning time while maintaining or improving decision quality
2Productivity
If traditional network analytics are used, then basic network performance monitoring is achieved, but scalable resource planning and accurate analytics are lacking
Solution Approach 1:
The system transforms traditional network analytics by incorporating machine learning parameters and algorithms that enable both scalable processing of large network datasets and high-precision resource planning analytics, simultaneously improving productivity and measurement precision
Solution Approach 2:
The patent combines traditional network analytics approaches with machine learning frameworks to create a composite analytics system that leverages the strengths of both methods, achieving both scalability and high precision in resource planning
3Adaptability or versatility
If the network accommodates increases in traffic and applications, then network capacity and functionality improve, but resource constraints and complexity increase
Solution Approach 1:
The system implements dynamic resource planning that automatically adapts to changing network conditions, traffic patterns, and application requirements, allowing the network to scale capacity while the ML system dynamically manages the increasing complexity of resource allocation
Data Source
AI summary
Method and apparatus for scalable machine learning (ML)-based frameworks for resource planning and recommendations for Enterprise Networks are disclosed. In some embodiments, a method for determining network health assessment and providing network planning and recommendations in wireless enterprise networks is provided. The method quantifies resource health of a plurality of network elements operating within the enterprise network by calculating a plurality of distance metrics between acceptable health parameters and observed network resource utilization values associated and corresponding to each selected network element. The method ranks each of the network elements based upon the distance metrics calculated for each network element. Factors that negatively impact the resource health of the network elements are ranked in accordance to how severely they impact the performance of the network elements. The method and apparatus provide suggestions and recommendations to improve network performance of the network elements. An apparatus for scalable machine-learning (ML)-based frameworks for resource planning in enterprise networks is also disclosed.