Meta-Application for Automated Software Deployment Problem Detection
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Solution Overview
Problem
Current software management tools lack automated analysis of monitored data, requiring manual triage for problem resolution and failing to continuously optimize systems, leading to increased Mean Time to Repair (MTTR) and limited Mean Time to Failure (MTTF), resulting in low system availability.
Innovation Solution
A meta-application that dynamically monitors and manages software deployments by creating an application model, using a knowledge base to detect problems, predict issues, and execute remedial actions, with automated encoding of knowledge and adaptive refinement based on outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual triage is used to analyze monitored data and pinpoint root-cause problems, then system availability is maintained through human expertise, but Mean Time to Repair (MTTR) increases and operational efficiency decreases
Solution Approach 1:
The system performs automated analysis of monitored data to detect problems and pinpoint root causes without requiring manual triage by IT staff. The meta-application automatically encodes knowledge, analyzes deployment data, and identifies root-cause problems, enabling the system to serve itself rather than relying on human operators for routine diagnostic tasks.
Solution Approach 2:
The patent replaces the mechanical process of manual data analysis and human triage with an automated computational system. The meta-application uses encoded knowledge and algorithms to automatically analyze monitored data, substitute human expertise with machine-based problem detection and root cause analysis, thereby reducing MTTR while maintaining diagnostic accuracy.
2Difficulty of detecting and measuring
If currently available monitoring tools are used to alert IT staff of problems, then problem detection is achieved, but the tools are extremely limited in their ability to continuously optimize systems or predict impending failures
Solution Approach 1:
The meta-application continuously analyzes deployment data to detect problems before they occur, such as predicting resource exhaustion or impending failures. By performing preliminary analysis and prediction, the system can take preventive actions before actual failures happen, extending beyond simple problem detection to proactive optimization and failure prevention.
Solution Approach 2:
The system continuously monitors deployment data and uses encoded knowledge to provide feedback on system health, performance trends, and potential issues. This continuous feedback loop enables the system to adaptively optimize deployments over time, learning from monitored data to improve detection accuracy and predict future problems based on emerging patterns.
3Loss of information
If IT staff manually analyze reports to extract conclusions about deployments, then comprehensive analysis is possible, but operational efficiency is significantly reduced due to time spent troubleshooting
Solution Approach 1:
The meta-application automatically performs the analysis function that would otherwise require IT staff to manually examine reports. By encoding knowledge about deployment patterns, problem indicators, and root cause relationships, the system independently analyzes monitored data, extracts meaningful conclusions, and presents findings without requiring human analysts to review raw reports.
Solution Approach 2:
The patent replaces the manual mechanical process of report analysis with an automated computational system. The meta-application uses encoded knowledge bases and analysis algorithms to automatically process deployment data, identify patterns, and extract conclusions, substituting human analytical effort with machine-based processing that maintains comprehensive analysis capability while dramatically improving operational efficiency.
Data Source
AI summary
A method of encoding knowledge is disclosed, which can be used to automatically detect problems in software application deployments. The method includes accessing a source of knowledge describing a problem known to occur in deployments of a particular software application, and which identifies a plurality of conditions associated with the problem. An encoded representation of the knowledge source is generated according to a predefined knowledge encoding methodology. The encoded representation is adapted to be applied automatically by a computer to analyze data representing a current state of a monitored deployment of the software application to detect whether the conditions and the problem exist therein. In various implementations, the encoded representation of the knowledge can include queries for deployment information, information concerning the relative importance of the conditions to a detection of the problem, and/or logical constructs for computing a confidence value in the existence of the problem and for determining whether to report the problem if some of the conditions are not true. The knowledge source can comprise a text document (such as a knowledge base article), a flowchart of a diagnostic troubleshooting method, and the like. Also disclosed are methods of at least partially automating the encoding process.