Automated Network Agent Diagnostic Tool for Distributed Systems
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Solution Overview
Problem
Large-scale distributed computer networks face challenges in identifying and locating malfunctioning components, which can cause delays and errors in network communications, making it difficult to diagnose the affected area.
Innovation Solution
A platform with an automated network agent module and big-data analysis module that deploys and runs diagnostic scripts on endpoints to compile diagnostic data, including success status, latency, and response size measurements, and generates reports to detect performance issues and flag underperforming network paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual diagnostic methods are used in large-scale distributed networks, then diagnostic accuracy may be sufficient for small networks, but the time and resources required increase exponentially with network size
Solution Approach 1:
The network is divided into multiple segments or zones, with diagnostic agents deployed at various levels (endpoints, intermediate nodes, core nodes). Each segment performs localized diagnostics, and results are aggregated upward, preventing the need for centralized control of every diagnostic operation across the entire network.
Solution Approach 2:
Automated diagnostic agents serve as intermediaries between network components and administrators. These agents collect data, analyze performance metrics, and present findings in standardized formats, reducing the time administrators would otherwise spend manually investigating network issues across distributed systems.
2Measurement precision
If comprehensive monitoring of all network paths is implemented, then diagnostic precision improves, but the computational resources and data processing requirements increase significantly
Solution Approach 1:
The system implements monitoring at multiple levels of detail - comprehensive monitoring at critical nodes and paths, and summarized monitoring at less critical areas. Diagnostic agents collect detailed metrics only when anomalies are detected or during scheduled deep-dive analyses, rather than continuously at full granularity across the entire network.
Solution Approach 2:
Multiple diagnostic data sources (endpoint metrics, intermediate node statistics, core network performance data) are merged and correlated by the analysis module. This consolidation allows the system to achieve high diagnostic precision by combining partial information from various sources rather than requiring complete independent monitoring of every network path.
3Productivity
If automated diagnostic agents are deployed at all endpoints, then diagnostic speed improves, but the complexity of deploying and managing the agents increases
Solution Approach 1:
The diagnostic agents are designed as universal, multi-functional components that can operate at different levels of the network hierarchy. The same agent architecture serves endpoints, intermediate nodes, and core systems, with capabilities to perform both localized diagnostics and contribute to network-wide analysis, simplifying deployment and management.
Solution Approach 2:
The agents are designed to be self-configuring and self-managing to the extent possible. They automatically register with the analysis module, configure their own monitoring parameters based on local network characteristics, and report their status and capabilities, reducing the manual overhead for deploying and managing large numbers of agents.
Data Source
AI summary
Systems and methods are provided for a remotely-deployed, automated, computer network diagnostic tool. Methods may include deploying a diagnostic script from a central network node to multiple network endpoints via an automated network agent tool, and running the diagnostic script on each of the multiple endpoints. Running the script on each endpoint may include compiling a set of URLs, compiling a set of proxy servers, testing a plurality of network paths, and generating a set of diagnostic data for each endpoint. Diagnostic data may include a success status, a latency performance score, and a response size measurement. Methods may include collating the sets of diagnostic data, generated from the multiple endpoints, into a collated set of diagnostic data. Methods may include analyzing the collated set of diagnostic data, via a big-data analysis tool, based on a set of performance metrics. Based on the analyzing, methods may include generating a diagnostic network report, and/or a flag when a network performance issue is detected.