ML-Based Device Configuration Issue Resolution
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Solution Overview
Problem
Conventional device management techniques rely on manual, time-consuming, and error-prone troubleshooting methods, leading to duplicative efforts and inefficiencies in resolving configuration-based issues across multiple devices, which can introduce security risks and outages.
Innovation Solution
The implementation of machine learning models to automatically determine configuration-based issue resolutions across multiple devices by training models on historical data, identifying similar devices, and performing automated actions based on predicted resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual troubleshooting methods are used to resolve configuration-based issues, then device issues can be addressed, but the process becomes time-consuming and error-prone with duplicative efforts across multiple devices
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical device data and configuration information before issues occur. The models are pre-trained to recognize patterns and predict configuration-based issues, enabling proactive identification and resolution before problems manifest across multiple devices
Solution Approach 2:
The system implements self-service by enabling automated issue detection and resolution through machine learning models that independently analyze device configurations, identify potential issues, and apply corrections without manual intervention. The models continuously learn from historical data and automatically resolve configuration mismatches across devices
2Productivity
If manual troubleshooting is performed reactively in response to user communications, then specific device issues can be addressed, but duplicative efforts occur across multiple devices
Solution Approach 1:
The system applies universality by using a single machine learning model framework that can identify and resolve configuration-based issues across multiple different device types and configurations. The model is trained on diverse historical data from various devices and can generalize patterns to prevent duplicative troubleshooting efforts across different device instances
Solution Approach 2:
The system performs preliminary analysis by continuously monitoring device configurations and using trained models to predict potential issues before they occur. This proactive approach identifies configuration mismatches across multiple devices simultaneously, resolving issues before user communications arise and eliminating reactive duplicative efforts
3Ease of operation
If configuration mismatches and software version discrepancies are not managed, then device operation continues, but security risks and vulnerability risks are introduced
Solution Approach 1:
The system implements feedback by continuously monitoring device configurations and comparing them against known secure configurations and software version requirements. The machine learning models analyze configuration data in real-time, provide feedback on detected mismatches or vulnerabilities, and automatically apply corrections to maintain security reliability while ensuring continuous device operation
Solution Approach 2:
The system performs preliminary security checks by using trained models to identify potential configuration-based security risks before they can be exploited. The models continuously analyze device configurations and proactively apply corrections to prevent security vulnerabilities from arising, maintaining both operational continuity and security reliability
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically determining configuration-based issue resolutions across multiple devices using machine learning models are provided herein. An example computer-implemented method includes training, using historical data related to device information and device configuration information from a set of devices, multiple machine learning models; determining, in connection with input data associated with a given device from the set of devices, a device issue and a corresponding device issue resolution, by processing the input data using at least a first of the machine learning models; identifying additional devices within the set of devices that are similar to the given device by processing the input data using at least a second of the machine learning models; and performing, based on the determined device issue resolution, automated actions in connection with the given device and at least a portion of the identified additional devices.