Fault Analysis Using Empirical Architecture Analytics
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Solution Overview
Problem
Modern software applications and systems face challenges in ensuring reliability and availability due to complex and dynamic nature, leading to critical production failures that are often caused by architectural quality degradation and are difficult to predict and reproduce.
Innovation Solution
A method and system for fault analysis and prediction using empirical architecture analytics, which involves obtaining data from various sources, associating it with identifiers, generating user workflows, and determining fault identifications or predictions based on the workflow and associated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developers and architects make design and architecture choices for software systems, then the system becomes more sophisticated and functional, but the architecture quality degrades over time leading to critical production failures
Solution Approach 1:
The system performs preliminary analysis of design and architecture choices by collecting empirical data from production environments before critical failures occur. It establishes baseline architecture quality metrics and continuously monitors deviations, enabling proactive identification of architectural degradation trends before they lead to system failures.
Solution Approach 2:
The system implements continuous feedback loops by collecting runtime data from production systems, analyzing architecture quality metrics, and providing insights back to developers and architects. This feedback mechanism enables iterative improvement of architectural decisions based on actual production performance and failure patterns.
2Ease of manufacture
If testers create test inputs without knowledge of actual system usage patterns, then testing can be performed in controlled environments, but test inputs do not adequately address real-world usage conditions and failure modes
Solution Approach 1:
The system creates accurate copies of real-world usage patterns by collecting and analyzing empirical data from production environments. It replicates actual user workflows, system interactions, and usage conditions in test environments, enabling testers to validate systems under realistic conditions without requiring production systems for testing.
Solution Approach 2:
The system performs preliminary analysis of actual usage patterns from production data before testing begins. It identifies common workflows, edge cases, and failure modes in advance, enabling testers to design more effective test cases that reflect real-world conditions without needing to observe production systems during testing.
3Reliability
If the system monitors and analyzes data from multiple sources to identify architecture quality parameters, then fault prediction capability improves, but the complexity of data collection and analysis increases
Solution Approach 1:
The system implements a universal data collection framework that can gather information from multiple diverse sources including application logs, system metrics, user feedback, and third-party services. This multi-functional approach consolidates various data collection mechanisms into a unified system that handles different data types and sources through common processing pipelines.
Solution Approach 2:
The system introduces intermediary components that act as mediators between diverse data sources and the analysis engine. These intermediaries standardize data formats, filter relevant information, and pre-process data before it reaches the core analysis system, reducing the complexity burden on the main fault prediction engine.
4Adaptability or versatility
If critical production failures are caused by causal sequences under specific usage conditions, then system functionality can be enhanced, but these failures become difficult to envisage during design and testing
Solution Approach 1:
The system implements continuous feedback monitoring that tracks causal sequences of events in production environments. By analyzing temporal patterns and correlations between different system events, it identifies failure precursors that result from specific usage conditions, enabling detection of issues that would be difficult to anticipate during design and testing phases.
Solution Approach 2:
The system performs preliminary analysis of causal event sequences by collecting and storing detailed information about system operations and interactions before failures occur. It establishes baselines of normal causal patterns and enables retrospective analysis to identify failure modes that emerge from specific usage conditions without requiring prior knowledge of those conditions.
Data Source
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AI summary
A method and system for fault analysis and prediction in an enterprise environment is described. In one embodiment, a method includes obtaining data from a plurality of sources in the enterprise environment. The plurality of sources includes at least one or more systems, users, or applications. The obtained data is associated with identifiers that include a theme selected from a set of themes and one or more keywords that are specific to each theme. The method includes generating a workflow for a user based on a session identifier and/or timestamps associated with activity by the user. The workflow identifies a time-based sequence of interactions by the user with the at least one or more systems or applications in the enterprise environment. The method also includes determining at least one fault identification or fault prediction based on the workflow and identifiers associated with the obtained data that corresponds to the workflow.