Network Device Management via Proactive Event Inference
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Solution Overview
Problem
Managing network devices in modern networks is challenging due to increased complexity and the difficulty in predicting post-deployment conditions, leading to buggy behavior and lack of visibility into network health parameters, with existing debugging techniques being reactive and limited to individual devices.
Innovation Solution
A learning-based management platform that monitors network device parameters, proactively predicts events, and automatically performs actions to mitigate potential errors, enabling proactive management and resource conservation by determining events, rule sets, and performing actions based on parameter information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive debugging techniques are used for individual network devices, then device-specific issues can be identified, but network-wide visibility and proactive error prevention are limited
Solution Approach 1:
The patent combines multiple individual device monitoring functions into a unified network management system. The inference engine aggregates data from multiple network devices and correlates events across the network, transforming isolated device debugging into comprehensive network-wide visibility and proactive error prevention.
Solution Approach 2:
The inference engine acts as an intermediary between raw network device data and actionable insights. It receives parameter information from network devices, applies logical rules to infer events and correlations, and generates proactive error predictions, bridging the gap between device-level data and network-wide understanding.
2Reliability
If proactive event prediction is implemented across the network, then network device failures can be prevented, but system complexity increases
Solution Approach 1:
The management system is segmented into modular functional components: data collection from network devices, inference engine with rule sets for event detection, correlation mechanisms for multi-device analysis, and action determination modules. This segmentation allows complex proactive prediction while maintaining manageable system architecture.
Solution Approach 2:
The inference engine automatically processes network device parameters, applies logical rules to infer events, and determines corrective actions without manual intervention. The system self-manages the complexity of proactive monitoring by autonomously analyzing data and generating predictions, reducing the operational burden despite increased system capabilities.
3Loss of information
If comprehensive parameter monitoring is performed across all network devices, then network health visibility improves, but processor and memory resources are consumed
Solution Approach 1:
The system monitors all network device parameters but applies selective processing through the inference engine. Not all parameters trigger full analysis - the system uses logical rules to infer events only when relevant parameter combinations indicate potential issues, performing partial analysis on high-risk conditions while maintaining comprehensive data collection.
Solution Approach 2:
The inference engine applies different processing intensity to different network devices and parameters based on local conditions. Devices or parameters showing anomalous patterns receive more intensive analysis, while normal operations use lighter processing, optimizing resource consumption while maintaining visibility where it matters most.
Data Source
AI summary
A device may receive, from a set of elements of a set of network devices, information associated with a set of parameters. The device may determine an event based on the set of parameters. The device may determine a rule set based on the event. The device may determine other events that are associated with the event based on the rule set. The device may determine statuses of the other events based on the information associated with the set of parameters. The device may evaluate a rule, of the rule set, based on the event and the statuses of the other events. The device may determine an action to be performed based on evaluating the rule. The device may cause the action to be performed in association with the element of the set of elements.