Automated Model Evolution for Distributed Software Systems
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Solution Overview
Problem
Monitoring and updating models of distributed software systems is time-consuming and error-prone, especially when the systems change or become outdated, as manual updates are required to reflect new transactions and behaviors.
Innovation Solution
An automated model evolution system that evaluates log data for discrepancies, generates candidate models using templates, and updates the system model based on goodness of fit criteria, ensuring efficient and accurate model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updating of system models is used, then model accuracy can be maintained, but time consumption increases and errors occur
Solution Approach 1:
The system automatically evaluates log data against the system model to identify discrepancies and generates updated models without requiring manual intervention. The automated model evolution system serves itself by detecting changes in transaction behavior and updating the model accordingly, eliminating the need for manual model maintenance while ensuring continuous accuracy.
Solution Approach 2:
The system continuously monitors log data and compares it against the current system model to identify discrepancies. This feedback loop enables the system to detect when the model becomes outdated and automatically triggers model regeneration based on the discrepancy analysis, ensuring the model remains accurate without manual updates.
2Reliability
If manual updating of system models is used, then model accuracy can be maintained, but error rate increases
Solution Approach 1:
The automated system eliminates human error by performing model updates through algorithmic processes. The system self-evaluates log data, automatically identifies discrepancies between expected and actual behavior, and generates corrected models without manual intervention, thereby eliminating errors associated with manual model maintenance.
Solution Approach 2:
The system replaces the mechanical process of manual model updating with an automated computational process. Instead of humans manually reviewing and updating models, the system uses automated algorithms to evaluate log data, identify discrepancies, and generate updated models, thereby eliminating human errors in the process.
3Productivity
If automated model evolution is implemented, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The automated model evolution system is divided into distinct functional modules: log data evaluation, discrepancy identification, candidate model generation, and model validation. This segmentation allows each component to perform its specific function independently, making the overall complex system more manageable and easier to implement while maintaining high update efficiency.
Solution Approach 2:
The system introduces an intermediary evaluation process that compares log data against the current model to identify discrepancies. This intermediary step acts as a mediator between the raw log data and the final model update, enabling automated evolution while keeping the complexity organized and controllable through structured comparison and validation processes.
Data Source
AI summary
A method includes performing, on a processor, evaluating log data; determining at least one discrepancy between the log data and a system model; generating a candidate model based on the discrepancy and a model template; and updating the system model based on the candidate model.