Predictive Logical Markers for Distributed File System Failure Recovery
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Solution Overview
Problem
In distributed file processing environments, server failures due to network issues, hardware or software malfunctions, or power outages lead to challenges in resuming file processing from the exact point of interruption, especially when multiple clients access the same file simultaneously.
Innovation Solution
The integration of artificial intelligence/machine learning (AI/ML) with a distributed file system (DFS) and the C++ programming language to predict logical markers of file fragments across multiple nodes, enabling efficient resumption of file processing and seamless access to files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional distributed file processing is used without AI/ML prediction, then the system structure is simpler, but the time to locate and process data blocks increases significantly after server failure
Solution Approach 1:
The AI/ML model predicts the logical marker (location of next data block) before the actual file processing occurs. By pre-calculating and storing predicted block locations during normal operation, the system can rapidly resume processing after server failure without time-consuming searches, thus reducing time loss while accepting increased system complexity
Solution Approach 2:
An AI/ML prediction engine is introduced as an intermediary component between the distributed file system and the processing logic. This intermediary analyzes processing patterns and predicts data block locations, acting as a mediator that reduces the time complexity of block location while adding computational overhead to the overall system
2Productivity
If AI/ML prediction is implemented to predict logical markers, then the time and computational resources to locate data blocks are reduced, but the device complexity increases
Solution Approach 1:
The system performs preliminary prediction of logical markers using AI/ML models during normal operation. By pre-computing and caching predicted block locations, the system enables rapid resumption of file processing after failures, thereby improving productivity while managing the added complexity through proactive rather than reactive processing
Solution Approach 2:
The patent changes the operational parameters of the file system by introducing predictive logical markers alongside traditional block addressing. This parameter change allows the system to switch between normal processing mode and failure recovery mode, improving productivity during recovery while the added complexity is confined to the prediction mechanism
3Reliability
If the system checks for previous read information and triggers AI engine, then file processing resumption is optimized, but the operational complexity increases
Solution Approach 1:
The system implements a feedback mechanism where the AI engine continuously monitors processing patterns and updates its predictions based on actual file access behavior. This feedback loop improves reliability by making the prediction model more accurate over time, while the automated nature of the feedback reduces the perceived operational complexity for users
Solution Approach 2:
The AI-driven prediction system operates autonomously to detect server failures and trigger appropriate recovery actions without requiring manual intervention. The system self-manages the complexity of failure detection, logical marker prediction, and processing resumption, thereby improving reliability while maintaining ease of operation for end users
Data Source
AI summary
This disclosure presents a system for rectifying server failures in distributed file systems using predictive logical markers. The system begins by receiving user details, a file name, and a block address via a distributed file system (DFS) client. If previous read information exists for the given file and user, a trained machine learning (ML) model predicts logical markers for file fragments. The ML model then transmits remaining block addresses to the DFS client. Concurrently, the system facilitates a data input stream, communicating predicted block addresses between the DFS client and the ML model. Block location information is retrieved from a distributed ledger via a primary node. The data input stream is processed via a secondary node, allowing efficient rectification of server failures.