Distributed Network Performance Management via Client Offloading
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Solution Overview
Problem
Current wireless network performance management methodologies are limited in their ability to obtain accurate data, integrate data for overall network performance, identify and troubleshoot areas of poor performance, and are reliant on central servers that lead to bandwidth and cost issues.
Innovation Solution
A distributed network performance management system that offloads service quality testing, reporting, and troubleshooting to wireless client devices, utilizing spare computing power and storage to reduce cloud operation costs and improve data collection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a central server performs network performance management, then control and coordination is simplified, but bandwidth requirements and data storage costs increase
Solution Approach 1:
The patent divides network performance management into distributed segments across multiple client devices. Each client device independently performs service quality testing and data collection, eliminating the need for a single centralized server to handle all processing. This segmentation reduces the burden on any single server while distributing computational tasks across the network edge.
Solution Approach 2:
Client devices perform self-testing and self-reporting of network performance parameters. The distributed testing application on each client device autonomously conducts service quality measurements and communicates results to the control server, reducing the need for centralized server-initiated testing and data collection operations.
2Device complexity
If a central server collects all network performance data, then centralized analysis is simplified, but data processing costs and bandwidth consumption increase
Solution Approach 1:
The patent extracts data processing capabilities from the centralized server and embeds them within client devices. The distributed testing application performs local analysis and filtering of network performance data, extracting only essential metrics for transmission to the control server. This reduces the volume of data requiring centralized processing and lowers bandwidth consumption.
Solution Approach 2:
The system performs partial data processing at the client level rather than attempting to collect and process all raw data centrally. By processing only the necessary performance metrics locally and transmitting summarized results, the system avoids excessive bandwidth consumption while maintaining adequate analysis capabilities.
3Measurement precision
If network performance testing is performed frequently with high granularity, then measurement accuracy improves, but bandwidth requirements and data collection overhead increase
Solution Approach 1:
The patent implements dynamic adjustment of testing frequency and granularity based on network conditions and service level agreements. The system adapts measurement parameters in real-time, increasing detail when problems are detected and reducing sampling when performance is acceptable, thereby optimizing the balance between measurement accuracy and data collection overhead.
Solution Approach 2:
The system changes testing parameters such as sampling frequency, test duration, and data granularity based on network conditions and priority levels. High-priority services receive more frequent and detailed measurement, while lower-priority services are measured less frequently, allowing accurate measurement of critical performance metrics without proportionally increasing overall data collection volume.
Data Source
AI summary
A distributed network performance management system and method that distributes a large portion of the network performance management to wireless client devices connected to the network. Rather than rely on a central server to perform the bulk of network performance management, a distributed network performance management system offloads much of the work of service quality testing, reporting, and troubleshooting to wireless client devices that are connected to the network. It utilizes spare computing power and storage space on the wireless client devices to reduce the cloud operation costs of the system including such things as bandwidth requirements, data storage requirements, and data processing requirements.