Calling Context Memory Map for Production Memory Monitoring
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Solution Overview
Problem
Existing memory monitoring techniques are inefficient for deployment stages due to high performance costs and inability to directly apply debugging methods, as they rely on slow and storage-intensive stack walking and lack sufficient context for root-cause analysis.
Innovation Solution
Implementing a computer-implemented method for memory monitoring using a calling context memory map and semantic extraction, which encodes calling context information for efficient storage and analysis, allowing for detection of memory anomalies like leaks and bloated memory without requiring source code, by generating a calling context memory map, extracting semantic information, and augmenting it for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If stack walking is used to extract calling context information, then memory monitoring can provide detailed context for root-cause analysis, but the speed and storage efficiency deteriorate
Solution Approach 1:
The patent extracts only the essential calling context information (function names, line numbers, file names) at memory allocation points and stores it efficiently in a memory map structure. This selective extraction avoids the need for complete stack walking while retaining sufficient context for root-cause analysis of memory errors.
Solution Approach 2:
The patent performs preliminary extraction and encoding of calling context information at the time of memory allocation, rather than performing full stack walking when errors occur. By preparing and storing the context information upfront in the memory map, the system achieves fast error detection without repeated expensive stack walking operations.
2Loss of information
If raw calling context is stored for memory profiling, then detailed memory analysis is possible, but storage efficiency and runtime performance deteriorate
Solution Approach 1:
The patent extracts only the necessary calling context fields (function name, line number, file name) and stores them in a compact structured format within the memory map. This selective extraction reduces storage requirements significantly compared to storing complete raw call stacks, while maintaining sufficient detail for memory error analysis.
Solution Approach 2:
The patent transforms the calling context information into a standardized, compact parameter format that can be efficiently stored and queried. By normalizing the context data structure and using efficient data types, the system minimizes storage requirements while preserving analytical capability.
3Reliability
If debugging memory profiling techniques are applied in deployment stage, then memory problem detection is possible, but system performance deteriorates
Solution Approach 1:
The patent implements a lightweight memory monitoring mechanism that operates transparently within the application itself, using standard memory allocation hooks and a compact memory map structure. This self-service approach enables memory error detection without requiring external profiling tools or heavy instrumentation that would degrade system performance.
Solution Approach 2:
The patent uses a lightweight memory map data structure that is inexpensive to create and maintain, storing only essential calling context information. This disposable-like structure can be quickly instantiated and destroyed without significant overhead, making it suitable for production environments where performance is critical.
Data Source
AI summary
A computer implemented method provides efficient monitoring and analysis of a program's memory objects in the operation stage. The invention can visualize and analyze a monitored program's data status with improved semantic information without requiring source code at runtime. The invention can provide higher quality of system management, performance debugging, and root-cause error analysis of enterprise software in the production stage.