Predictive Connectivity Monitoring for Proactive Issue Mitigation
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Solution Overview
Problem
Conventional approaches to device connectivity monitoring in complex networks are largely reactive, failing to proactively detect and mitigate potential connectivity issues, which can lead to service degradation and penalties due to missed service-level agreements.
Innovation Solution
A device connectivity monitoring system utilizing a machine learning model to predict connectivity issues based on device configurations and events, generating a connectivity score, and initiating automated actions to mitigate potential issues before they occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reactive monitoring approaches are used, then device connectivity issues are detected only after they occur, but service degradation and penalties due to missed service-level agreements happen
Solution Approach 1:
The system performs preliminary actions by continuously collecting connectivity configuration data and event data, training machine learning models in advance, and generating connectivity scores before actual connectivity issues occur. This proactive approach enables the system to predict and prevent connectivity problems, thereby improving service reliability and reducing downtime.
2Reliability
If machine learning models are used to predict connectivity issues, then proactive detection and mitigation of connectivity problems is achieved, but data processing complexity increases
Solution Approach 1:
The system implements self-service by automatically collecting connectivity configuration data and event data from devices, autonomously training machine learning models using the collected data, and generating connectivity scores without requiring manual intervention. This automation reduces the operational complexity despite the advanced predictive capabilities.
3Reliability
If automated actions are initiated based on connectivity scores, then connectivity issues are mitigated proactively, but automation extent increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring device connectivity status, comparing actual connectivity outcomes with predicted connectivity scores, and using this feedback to retrain and improve the machine learning models. This closed-loop feedback system enables the automation to adapt and improve over time while maintaining reliability.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for device connectivity monitoring are provided herein. An example computer-implemented method includes obtaining data, over a time period, associated with (i) a connectivity configuration of a device and (ii) at least one set of events related to a current connectivity state of the at least one processing device, where at least a portion of the data is obtained from the device and one or more components involved in communications with the device. The method includes generating, by a machine learning model, a connectivity score for the device based on the data, where the connectivity score indicates a probability of a device experiencing at least one connectivity issue. The method also includes initiating, in response to the connectivity score satisfying at least one designated threshold, one or more automated actions to mitigate the connectivity issue.