Automated Heap Dump Analysis for Out-of-Memory Error Detection
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Solution Overview
Problem
In cloud computing environments, manually processing and analyzing heap dumps to identify and debug errors is tedious and inefficient, especially since a single issue can propagate to numerous application servers, leading to duplicate errors.
Innovation Solution
A method and system that automatically process and analyze heap dumps using machine learning to identify suspect classes and duplicate out-of-memory errors by rebuilding object dependencies, calculating memory usage, and utilizing suspect identification scores to compare signatures across heap dumps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing and analysis of heap dumps is performed, then developers can identify and debug errors, but the process becomes tedious and time-consuming
Solution Approach 1:
The system enables self-service error detection by automatically analyzing heap dumps without requiring manual developer intervention. The error detection system autonomously processes heap dump data, identifies suspect classes, and generates error reports, allowing the system to serve itself in the debugging process.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of developers manually examining heap dumps, the system uses automated algorithms to process heap dump data, calculate memory usage statistics, and identify errors, substituting human mechanical analysis with automated computational analysis.
2Measurement precision
If each heap dump is processed individually, then detailed analysis is possible, but the same issue propagates to numerous application servers causing duplicate errors
Solution Approach 1:
The system merges the analysis of multiple heap dumps by collecting heap dump data from numerous application servers and processing them together. The error detection system combines heap dump information across servers, identifies common suspect classes, and consolidates duplicate errors into single error reports, merging individual analyses into a comprehensive multi-server error profile.
Solution Approach 2:
The error detection system performs multiple functions simultaneously: it processes individual heap dump analysis while also performing cross-server error correlation, duplicate detection, and aggregated reporting. This multi-functional approach allows the system to maintain detailed analysis capabilities while efficiently handling errors across numerous application servers.
3Productivity
If automated error identification is implemented, then debugging time is reduced, but system complexity increases
Solution Approach 1:
The system segments the complex error detection process into distinct modular components: heap dump data collection, suspect class identification, memory usage calculation, duplicate error detection, and error reporting. Each module performs a specific function, making the overall complex system manageable through functional segmentation and independent module development.
Data Source
AI summary
A method for identifying errors in code is provided. The method may include rebuilding object dependencies from a heap dump, calculating memory usage of each object, identifying top consumers of memory by object class, analyzing how much memory each class consumes with respect to how much other classes consume, building a corpus of data that may be used in a progressive machine learning algorithm, and identifying suspect classes. Additionally, the suspect classes and the memory usage statistics of the suspect classes may then be used as an identifying signature of the associated out of memory error. The identifying signature of the associated out of memory error may then be used to compare with the signatures of other out of memory occurrences for identifying duplicate error occurrences.