Inference Engine for Network Fault Detection
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Solution Overview
Problem
Data centers face scalability issues in managing their rapidly growing network infrastructure, leading to operational difficulties in troubleshooting and maintaining network devices, especially due to complexity and incomplete information availability, resulting in potential service disruptions and customer dissatisfaction.
Innovation Solution
Implementing a model-based system that captures device signatures, failure scenarios, and capacity models, utilizing an inference engine with forward-chaining rules to automatically identify issues and take remedial actions, and continuously monitor devices to prevent or mitigate failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual management methods are used for network infrastructure, then operational control is maintained, but scalability and efficiency deteriorate as the network grows rapidly
Solution Approach 1:
The patent introduces an inference engine as an intermediary between network devices and operators. This engine collects data from multiple sources, performs automated analysis using forward-chaining rules, and generates diagnostic information, thereby mediating the complexity between rapidly growing network infrastructure and human operators.
Solution Approach 2:
The system enables self-service through automated data collection, analysis, and diagnostic generation. The inference engine autonomously monitors network devices, detects anomalies, and provides troubleshooting information without requiring manual intervention for each issue, allowing the network to manage itself at scale.
2Reliability
If comprehensive monitoring of all network devices is implemented, then reliability improves, but information completeness and troubleshooting accuracy worsen due to data overload
Solution Approach 1:
The inference engine extracts only the most relevant diagnostic information from the vast amount of collected data. By using forward-chaining rules that specifically target failure scenarios and capacity issues, the system extracts critical insights while filtering out unnecessary data, maintaining information quality despite comprehensive monitoring.
Solution Approach 2:
The system implements feedback mechanisms where diagnostic information is continuously refined based on actual network performance and failure patterns. The inference engine learns from collected data and adjusts its analysis to provide increasingly accurate diagnostic information, improving reliability while managing information quality.
Data Source
AI summary
In a provider network, data indicative of an operational state of the computing devices of the provider network is processed by an inference engine. The inference engine is configured to model operational characteristics of the computing devices of the provider network. The inference engine determines a potential fault condition for one of the computing devices of the provider network. A remedial action is invoked.