Automated Network Diagnostics via Traffic Simulation
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Solution Overview
Problem
Current network diagnostics methods are manual, time-consuming, and inefficient, often requiring significant guesswork due to the complexity of issues in live networks, leading to high costs and potential network outages.
Innovation Solution
Implementing a diagnostics system with automated diagnostics nodes that simulate real traffic and use scripts to obtain status information from network devices via various communications protocols, allowing for precise identification and diagnosis of issues without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual diagnostics methods are used to test network nodes, then operators can identify and resolve issues, but the process becomes time-consuming, expensive, and resource-intensive
Solution Approach 1:
The network nodes perform self-diagnostics by automatically generating test traffic, monitoring their own performance metrics, and identifying issues without requiring manual operator intervention. The system enables networks to diagnose and report their own problems through automated agents deployed at network nodes.
Solution Approach 2:
Manual diagnostic procedures are replaced with automated software agents and algorithms that systematically test network nodes, analyze traffic patterns, and identify issues through computational methods rather than human operators performing manual tests.
2Reliability
If comprehensive network testing is performed before deployment, then network issues can be detected early, but the testing process requires significant resources and time
Solution Approach 1:
Instead of exhaustive manual testing of all network parameters, the system implements targeted automated tests that focus on critical failure points and common issues. The automated agents perform selective diagnostics on specific network nodes and traffic patterns that are most likely to reveal problems.
Solution Approach 2:
The system dynamically adjusts testing parameters such as traffic volume, packet types, and test frequency based on network conditions and node characteristics. This allows comprehensive testing to be performed efficiently by adapting test intensity and scope to actual network needs rather than using fixed resource-intensive protocols.
3Productivity
If automated diagnostics systems are implemented, then diagnosis speed and accuracy improve, but system complexity increases
Solution Approach 1:
The automated diagnostics system is divided into independent modular agents that can be deployed individually at different network nodes. Each agent handles specific diagnostic functions locally, and results are aggregated by a central controller, allowing the system to scale without proportionally increasing overall complexity.
Solution Approach 2:
A central diagnostics controller acts as an intermediary between distributed network nodes and operators. It coordinates automated tests, collects data from multiple sources, analyzes results, and presents unified diagnostic information, simplifying the interface between complex automated systems and human users.
Data Source
AI summary
Methods, systems, and computer readable media for network diagnostics are disclosed. According to one method, the method occurs at a diagnostics controller implemented using at least one processor. The method includes configuring a plurality of diagnostics nodes to observe traffic behavior associated with a system under test (SUT). The method also includes observing, using the diagnostics nodes, traffic behavior associated with the SUT. The method further includes detecting, using the traffic behavior, a SUT issue. The method also includes identifying, using SUT topology information, a network node in the SUT associated with the SUT issue. The method further includes triggering one of the diagnostics nodes to obtain node related information from the network node, wherein the diagnostics node uses at least one communications protocol to poll the network node for the node related information. The method also includes diagnosing, using the node related information, the SUT issue.