Context Extraction Module for POSIX Data Access Analysis
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Solution Overview
Problem
Current POSIX-compliant storage systems do not obtain and store contextual information about data access operations, which is useful for analytical purposes such as trend analysis and optimizing data access efficiency.
Innovation Solution
A context extraction module intercepts data calls made through POSIX APIs, obtaining contextual information like process names, call stacks, and access types, and stores this information as metadata or in a database for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If contextual information is collected and stored for every data access operation through POSIX APIs, then analytical capabilities for trend analysis and performance optimization are improved, but system complexity and overhead increase
Solution Approach 1:
The patent introduces a context extraction module as an intermediary component that sits between the POSIX API layer and the kernel. This module intercepts data access calls, extracts contextual information (such as process ID, file path, access type), and stores it in a separate database without modifying the core kernel or POSIX API implementations. This intermediary approach enables comprehensive information collection while maintaining system modularity and minimizing complexity in critical system components.
Solution Approach 2:
The system is segmented into distinct functional components: the existing POSIX API layer, the new context extraction module, the kernel, and a separate database for storing contextual information. By separating the information collection and storage functions from the core file access mechanisms, the patent enables analytical capabilities without entangling system complexity in the critical path of data access operations.
2Loss of information
If contextual information is stored for all data access operations, then data access trend analysis capability is improved, but storage overhead and processing time increase
Solution Approach 1:
The context extraction module extracts only the essential contextual information needed for analysis (process ID, file path, access type, timestamp) from the data access calls, separating this metadata from the actual data access operations. By extracting and storing only the necessary information in a structured format, the system enables comprehensive trend analysis without the overhead of storing complete access logs or modifying the speed of actual data access operations.
Data Source
AI summary
A method performed by a computing system, includes executing an application, using a data call to an Application Programming Interface, the data call requesting access to a file stored on a storage system associated with the computing system, with a context extraction module, determining contextual information associated with the data call, through use of a library, causing a kernel to access the file according to the data call, storing the contextual information on the storage system, and performing an analysis on the contextual information, the analysis including determining an average size of a call stack when the data was accessed.