Automated Application Error Recovery via Pattern Matching
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Solution Overview
Problem
In networked computing environments, application errors often disrupt operations, and existing recovery methods require manual intervention by experts, leading to inefficiencies and delays, as similar errors across devices are addressed individually without immediate centralized solutions.
Innovation Solution
An agent associated with each computing device communicates error notifications to a central system, which compares them to stored error patterns and provides matching recovery actions, allowing for automated or guided recovery with minimal disruption, and learns from new errors to improve pattern recognition over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual expert intervention is used to recover applications from errors, then recovery accuracy can be maintained, but recovery time increases and productivity decreases
Solution Approach 1:
The system performs preliminary actions by collecting error logs, analyzing error patterns, and preparing recovery actions in advance. The error analysis module continuously monitors and stores error patterns in a database, so when an error occurs, the pre-prepared recovery action can be immediately applied without waiting for manual expert analysis, thus resolving the contradiction between maintaining accuracy and improving recovery speed
Solution Approach 2:
The system implements feedback mechanisms where error logs are collected from computing devices, analyzed to identify patterns, and used to generate recovery actions that are fed back to the system. This closed-loop feedback enables the system to learn from past errors and improve its automated recovery capabilities over time, maintaining reliability while reducing dependence on manual intervention
2Measurement precision
If centralized error analysis system is implemented, then error pattern recognition improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary error analysis module that acts as a mediator between computing devices and the centralized system. This module collects and pre-processes error logs locally before transmitting them to the centralized system, simplifying the architecture by distributing basic processing functions while maintaining centralized pattern recognition capabilities
Solution Approach 2:
The system is segmented into multiple independent components: error log collection agents on computing devices, error analysis modules that process logs, pattern recognition systems that analyze data, and recovery action generators. This segmentation allows each component to specialize in specific tasks, improving error pattern recognition while managing overall system complexity through modular design
3Productivity
If automated recovery actions are implemented, then productivity increases, but ease of operation decreases for complex errors
Solution Approach 1:
The system applies partial automation where routine, common errors are handled automatically through pre-defined recovery actions, while more complex or novel errors are escalated for manual review. This partial action approach maintains high productivity for common cases while preserving operator control and ease of operation for complex situations that require human judgment
Data Source
AI summary
Aspects of recovery of an application from an error. An error notification is received from an agent associated with an application, the error notification being indicative of an error encountered during execution of the application. The error notification is compared with error patterns stored in a database to determine a matching error pattern. Further, a recovery action is ascertained and the recovery action is provided to the agent to recover the application from the error.