Distributed Computer Systems for Intelligent Failure Reprocessing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distributed computer systems face challenges in maintaining concurrency, overcoming the lack of a global clock, and managing independent component failures, leading to repetitive failure data reprocessing that can cause a hung state and performance issues.
Innovation Solution
Utilizing augmented analytics and generative artificial intelligence to identify failure synchronization points, predict data failure trends, and determine criteria for successful reprocessing, while encrypting data for security and communicating with target systems to ensure availability before reprocessing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If failed interactions are reprocessed repeatedly, then data completeness is improved, but network traffic and CPU utilization increase causing performance issues
Solution Approach 1:
The system implements feedback mechanisms by analyzing failure patterns from historical data and using machine learning models to predict future failures. This feedback loop enables intelligent decision-making about reprocessing, allowing the system to avoid reprocessing interactions that are likely to fail again, thus reducing network traffic and CPU utilization while maintaining data completeness.
Solution Approach 2:
The system performs preliminary analysis of failure patterns using machine learning models before reprocessing failed interactions. By predicting potential failures in advance and identifying synchronization points that are likely to succeed, the system prepares optimal reprocessing strategies beforehand, avoiding wasted computational resources on doomed reprocessing attempts.
2Measurement precision
If augmented analytics and machine learning models are used to analyze failure data, then failure prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the failure analysis process into distinct machine learning models that each handle specific aspects: one model identifies data failures versus system failures, another predicts failure patterns, and a third determines synchronization points. This segmentation allows each model to be specialized and optimized, improving prediction accuracy while managing computational complexity through modular architecture.
3Stability of the object's composition
If synchronous orchestration is used to ensure coordination, then consistency is improved, but system responsiveness deteriorates
Solution Approach 1:
The system inverts the traditional synchronous orchestration approach by using asynchronous processing with intelligent retry mechanisms. Instead of blocking operations to ensure immediate consistency, the system processes interactions asynchronously and uses machine learning to determine optimal retry timing, achieving consistency without sacrificing responsiveness.
Data Source
AI summary
A system accesses data of a failed interaction with a target system from a queue and determines whether the failed interaction is a data failure or a system failure. For a data failure, the system determines a category and whether it can be fixed. If it can be fixed, the system updates the data and reprocesses the failed interaction based on the updated data. If it cannot be fixed, the system deletes the data from the queue and notifies the target system the category of the data failure. For a system failure, the system identifies a system trend of the target system and determines whether it can be fixed. If it can be fixed, the system determines a reprocessing schedule and reprocesses the failed interaction accordingly. If it cannot be fixed, the system deletes the data from the queue and notifies the target system of the system trend.