Adaptive Network Configuration Framework for QoE Optimization
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Solution Overview
Problem
Conventional network management systems focus primarily on maintaining Quality of Service (QoS) but often fail to ensure Quality of Experience (QoE) and adaptively manage network parameters, leading to issues such as configuration changes causing outages and inadequate security.
Innovation Solution
A decision intelligence (DI)-based computerized framework that self-manages network components and parameters by leveraging information on network capacity and activity to identify and configure optimal settings, ensuring both QoS and QoE, with adaptive configuration management, real-time monitoring, and enhanced security features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional network management tools are used to manually configure network parameters, then administrators can control network settings, but the system lacks adaptability and cannot ensure Quality of Experience (QoE)
Solution Approach 1:
The network management system implements self-service through automated configuration management that autonomously monitors network performance metrics, identifies optimization opportunities, and applies configuration changes without manual administrator intervention. The system self-adjusts network parameters based on real-time conditions to maintain both QoS and QoE standards.
Solution Approach 2:
The system dynamically changes network parameters such as bandwidth allocation, traffic routing, and device configuration settings based on monitored performance metrics and QoE requirements. Configuration management automatically adjusts these parameters in response to changing network conditions and application demands.
2Reliability
If manual network configuration management is performed, then specific network settings can be adjusted, but configuration changes may cause outages and reduce reliability
Solution Approach 1:
The configuration management system performs preliminary actions by monitoring network conditions and pre-planning configuration changes before they are applied. The system evaluates potential impacts of configuration changes and schedules them during optimal times to minimize disruption to network operations.
Solution Approach 2:
The system implements continuous feedback loops that monitor network performance after configuration changes are applied. If negative impacts are detected, the system automatically reverses changes or adjusts parameters to maintain network reliability and operational integrity.
3Reliability
If basic QoS management is implemented, then network traffic can be controlled, but Quality of Experience (QoE) for applications and devices cannot be ensured
Solution Approach 1:
The system replaces manual mechanical configuration processes with automated electronic monitoring and control mechanisms. Sensors and software agents continuously collect network performance data, and automated algorithms process this information to make configuration decisions, eliminating the need for manual administrator intervention.
Solution Approach 2:
The configuration management system provides multi-functionality by simultaneously managing QoS parameters, monitoring QoE metrics across multiple applications and devices, and coordinating configuration changes across the entire network infrastructure through a single integrated platform.
4Adaptability or versatility
If network parameters are statically configured, then system simplicity is maintained, but the network cannot adapt to changing conditions and applications
Solution Approach 1:
The system transitions from static to dynamic configuration by continuously monitoring network conditions and automatically adjusting parameters in real-time. Configuration settings become dynamic variables that adapt to changing traffic patterns, application requirements, and network conditions without requiring manual reconfiguration.
Data Source
AI summary
Disclosed are systems and methods that provide a computerized network management framework that adaptively configures network parameters/characteristics of a network at a location based on determined intelligence about the network, and/or devices and/or applications executing thereon. The disclosed framework can leverage information related to network capacity and coverage against network activity (e.g., upload/download, streaming, and the like) of devices connected to the network to determine i) which components and/or network activities are causing issues within a network and/or ii) how to configure a network to address/remedy such components and/or activities. Accordingly, the disclosed framework can effectuate control and modifications, and/or capabilities for identifying specific network parameters, firmware, software and/or hardware in order to realize specific configurations of the network to improve the network's quality, capacity and/or coverage, among other characteristics of the network and its operations.