Healing-as-a-Service Edge ML for System Remediation
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Solution Overview
Problem
Conventional system management approaches face challenges in remotely identifying and remediating issues across user systems due to incompatible software updates, latency issues, and security concerns, particularly in complex network environments where relationships between software layers are not considered.
Innovation Solution
The implementation of automated issue detection and remediation using healing-as-a-service techniques, which involve obtaining system configuration data, training machine learning models, and automatically performing configuration adjustments across multiple user systems to address issues while ensuring data security and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional system management approaches are used to remotely identify and remedy issues, then system updates can be deployed, but latency problems occur due to processing large amounts of data
Solution Approach 1:
The patent segments the system management function by deploying lightweight machine learning models directly on edge devices (user systems) rather than centralizing all processing. This allows local issue detection and remediation without requiring constant data transmission to centralized servers, thereby reducing processing latency while maintaining productivity.
Solution Approach 2:
The patent implements preliminary action by pre-training machine learning models with system configuration data and alert patterns before deployment. These pre-trained models can then immediately detect and remediate issues without requiring extensive real-time data processing, thus reducing latency while maintaining high productivity in issue resolution.
2Productivity
If conventional system management approaches access user data for issue remediation, then problems can be solved, but security and data privacy problems arise
Solution Approach 1:
The patent implements self-service by enabling user systems to autonomously detect and remediate their own issues using locally deployed machine learning models. The models process data locally without requiring external systems to access sensitive user data, thereby maintaining security and privacy while preserving full issue remediation capability.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between system issues and remediation actions. These models process and analyze system data locally, acting as a mediator that enables issue resolution without requiring direct access to sensitive user data by external systems, thus maintaining security while preserving productivity.
3Ease of manufacture
If static predetermined update schedules are used, then updates are applied systematically, but incompatible software updates are installed causing system issues
Solution Approach 1:
The patent transitions from static predetermined update schedules to dynamic, adaptive update management. Machine learning models continuously analyze system configuration data and alert patterns to determine the optimal timing and sequence of updates, adapting to each system's specific state and relationships between software layers, thereby maintaining reliability while preserving ease of deployment.
Solution Approach 2:
The patent implements feedback mechanisms where machine learning models continuously monitor system alerts and configuration data to evaluate the impact of updates. This feedback loop allows the system to learn from past update outcomes and adjust future update decisions, preventing incompatible updates while maintaining simple deployment processes.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automated issue detection and remediation across multiple user systems using healing-as-a-service techniques are provided herein. An example computer-implemented method includes obtaining system configuration data from at least a portion of multiple user systems within a network; obtaining an alert pertaining to an issue attributed to a first of the user systems; training a machine learning model related to user system issue detection using at least a portion of the system configuration data and data related to the alert; determining user system configuration adjustments related to remedying at least a portion of the issue, by processing the data related to the alert using the trained machine learning model; automatically performing the user system configuration adjustments in connection with the first user system; and sharing, using at least one healing-as-a-service component, the trained machine learning model with the user systems in the network.