Automated Root Cause Debugging Across Multiple Applications
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Solution Overview
Problem
Conventional debugging methods for errors in enterprise networks with interrelated applications are manual, time-consuming, inefficient, and concentrate institutional knowledge within certain individuals, leading to sequential and slow identification of root causes.
Innovation Solution
An automated debugging system, referred to as the 'Auto Debugger', performs parallel debugging operations across applications, receiving error metadata, and using a root cause analysis engine to identify local and global root causes through preconfigured debugging operations and error reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual debugging approaches are used, then application teams can review errors sequentially, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables self-service debugging by automatically executing preconfigured debugging operations across multiple applications without requiring manual intervention from application teams. The root cause analysis engine autonomously queries error metadata, executes debugging operations, and identifies root causes, allowing the system to debug itself rather than requiring human operators to manually review each error.
Solution Approach 2:
Debugging operations are preconfigured in advance before errors occur. The system stores preconfigured debugging operations that can be automatically executed when errors are detected, eliminating the need for manual setup and review during the debugging process. This preliminary configuration enables rapid automated execution of debugging tasks.
2Device complexity
If sequential debugging steps are performed by each application team, then responsibility can be clearly assigned, but the overall debugging process becomes slow and time-consuming
Solution Approach 1:
The system merges debugging operations across multiple applications into a single parallel execution process. Instead of having each application team perform debugging sequentially, the root cause analysis engine executes preconfigured debugging operations for all affected applications simultaneously, combining multiple debugging tasks into one unified parallel process that completes much faster.
Solution Approach 2:
The system transitions from sequential time-based debugging to parallel space-based debugging by executing multiple debugging operations concurrently across different applications. This dimensional change from sequential to parallel execution fundamentally alters the debugging process, enabling simultaneous investigation of errors across the entire application landscape rather than step-by-step through each application.
3Reliability
If manual debugging is performed by application teams, then institutional knowledge can be applied, but the knowledge remains concentrated within certain individuals
Solution Approach 1:
The system creates a digital copy of institutional knowledge through preconfigured debugging operations and error metadata templates. Instead of relying on individual experts' knowledge, the system captures and stores debugging procedures, query patterns, and analysis methodologies as reusable digital artifacts that can be automatically executed, preserving knowledge without concentrating it in specific individuals.
Solution Approach 2:
The system replaces the mechanical human-based debugging process with an automated computational system. Human expertise is encoded into preconfigured debugging operations that the root cause analysis engine executes automatically, substituting the mechanical action of manual review with automated data querying, execution, and analysis processes that maintain accuracy without human intervention.
4Productivity
If automated parallel debugging operations are executed, then system downtime is reduced and productivity improves, but the system complexity increases
Solution Approach 1:
The system segments the complex automated debugging process into discrete, manageable components: error metadata collection, preconfigured debugging operation definitions, root cause analysis engine execution, and result aggregation. By breaking down the overall system into these modular segments, the complexity becomes more manageable and the system more maintainable while achieving parallel processing capabilities.
Data Source
AI summary
Presented herein are systems and methods for determining root cause of errors during execution of multiple applications. Systems include at least one processor to detect a cause analysis instruction identifying one or more systems; receive error metadata associated with a first operation error; determine a first debug operation set based on the error metadata; determine a first result associated with a first debug operation of the first debug operation set; and determine a second result associated with a second debug operation based on the first result for the first debug operation. The one or more processors can generate an error report based on the first result and the second result, the error report indicating each result and a next debug operation set of one or more next debug operations.