Architecture Drift Detection System for Application Availability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In computing systems, application architecture drift occurs when the actual implementation deviates from the designed architecture pattern, leading to potential technical, financial, and reputational risks due to changes in requirements or during production, resulting in application unavailability or poor user experience.
Innovation Solution
A system and method for detecting architecture drift by obtaining architecture design metrics and data metrics from application monitoring systems, comparing them to determine deviations, and providing notifications to administrators to correct the drift before undesirable outcomes occur, thereby reducing downtime and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If architecture drift detection and monitoring systems are implemented, then application availability and reliability are improved, but device complexity and system overhead increase
Solution Approach 1:
The patent implements continuous monitoring of architecture metrics and comparison against defined patterns, creating a feedback loop that detects drift conditions and triggers notifications. This automated feedback mechanism improves reliability by ensuring architecture compliance without requiring manual intervention, while the systematic approach manages complexity through structured metric collection and pattern matching.
Solution Approach 2:
The patent introduces an intermediary monitoring system that acts as a mediator between the application infrastructure and architecture compliance requirements. This intermediary layer collects metrics, compares them against defined patterns, and generates notifications, thereby improving reliability through automated detection while containing complexity within a dedicated monitoring component rather than distributing it across the entire system.
2Manufacturing precision
If continuous architecture monitoring is performed, then manufacturing precision and architecture compliance are improved, but use of energy and computational resources increase
Solution Approach 1:
The patent monitors specific architecture-related metrics rather than all possible system parameters, applying partial action by focusing only on the critical attributes needed to detect drift conditions. This selective monitoring approach maintains architecture compliance detection capability while reducing the computational resources and energy required compared to comprehensive system-wide monitoring.
Solution Approach 2:
The patent creates simplified representations of architecture patterns as defined metrics and rules that can be efficiently compared against actual system state. By working with copied/abstracted architecture definitions rather than complex original specifications, the system achieves precise compliance detection with reduced computational overhead, as the comparison logic operates on standardized metric structures.
3Loss of time
If architecture drift detection is implemented early in the process, then loss of time for corrective action is reduced, but device complexity increases
Solution Approach 1:
The patent performs continuous architecture compliance checking as a preliminary action before drift conditions can cause significant harm. By proactively monitoring and detecting architecture deviations early in the process, the system minimizes the time required for corrective action. The monitoring infrastructure is established in advance and continuously validates architecture compliance, enabling rapid response to drift conditions.
Solution Approach 2:
The patent implements continuous feedback loops that provide real-time information about architecture compliance status. This feedback mechanism enables early detection of drift conditions and immediate notification to relevant personnel or systems, reducing the time required for corrective action. The systematic feedback approach manages complexity through automated alerting and structured metric comparison rather than requiring complex manual monitoring procedures.
Data Source
AI summary
Provided is an architecture drift detection system and method including: obtaining a first set of architecture design metrics associated with a first application; obtaining first set of data metrics associated with a first instance of the first application that is installed at a first server computing system; obtaining a second set of data metrics associated with a second instance of the first application that is installed at a second server computing system; determining, using the first set of data metrics and the second set of data metrics, that at least one of the first instance, the first server computing system, the second instance, or the second server computing system deviates from one or more architecture design metrics from the first set of architecture design metrics associated with the first application; and providing a deviation notification indicating a deviation from the one or more architecture design metrics.