Temporal Stack Trace Correlation for Resource Spike Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identifying specific code paths responsible for resource utilization spikes in complex applications with numerous threads and code paths is challenging, as current methods fail to pinpoint the root cause without a consistent baseline in production environments.
Innovation Solution
Collecting and analyzing stack tracing and profiling data in conjunction with resource utilization data, using a search tree structure to align and correlate code paths with resource spikes, and applying filtering and ranking techniques to identify contributing code paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sampling is performed to capture timing data from various code paths, then resource utilization monitoring is achieved, but difficulty in isolating primary contributors to resource spikes increases
Solution Approach 1:
The patent segments the monitoring system into multiple components: a sampling component that collects timing data from code paths, a processing component that analyzes the sampled data, and a reporting component that identifies primary contributors. This segmentation allows the system to maintain comprehensive monitoring while systematically isolating the specific code paths causing resource spikes through hierarchical analysis.
2Difficulty of detecting and measuring
If CPU profiling is run during spike events, then code path identification is attempted, but root cause pinpointing fails without consistent baseline
Solution Approach 1:
The patent implements preliminary action by continuously sampling and storing timing data from all code paths during normal operation, establishing a baseline dataset before resource spikes occur. This pre-collected data enables reliable root cause identification during spike events by comparing against the established baseline, eliminating the need for consistent manual baselines.
Solution Approach 2:
The system incorporates feedback mechanisms where the processed timing data from sampled code paths is continuously compared against resource utilization metrics. This feedback loop automatically identifies deviations from normal behavior and pinpoints the specific code paths contributing to resource spikes, providing reliable root cause analysis without manual intervention.
3Loss of information
If comprehensive stack tracing is implemented across thousands of threads, then complete code path coverage is achieved, but data analysis complexity increases
Solution Approach 1:
The patent extracts only the essential timing information from comprehensive stack traces, focusing on start and end timestamps of function calls rather than processing the entire trace data. This extraction approach maintains complete code path coverage while significantly reducing data analysis complexity by isolating the critical temporal metrics needed for identifying resource spike contributors.
Solution Approach 2:
The system changes the parameters of data representation by transforming detailed stack trace information into standardized timing metrics (start time, end time, duration). This parameter transformation simplifies the analysis complexity while preserving complete code path coverage, enabling efficient processing of data from thousands of threads through consistent temporal measurement.
Data Source
AI summary
Apparatus and method for identifying code path contributors to resource utilization spikes. For example, in some implementations, stack trace data and profiling data are sampled from a plurality of code paths of an application to generate a plurality of code path samples, where the sampling is performed across a series of temporal windows. Processor utilization data is also sampled across a corresponding series of temporal windows. Correlations are detected between the code path samples and occurrences of processor utilization spikes to generate correlational scores for the plurality of code paths. A ranking of code paths is then generated in accordance with the correlational scores in which code paths relatively higher in the ranking are more likely to contribute to the occurrences of the processor utilization spikes than code paths relatively lower in the ranking.