Critical Path Analysis of Cloud Application Trace Files
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Solution Overview
Problem
Conventional performance analysis tools face challenges in identifying critical paths within trace data due to high memory usage and inability to isolate activities on the critical path, especially for large traces, which hinders effective performance analysis and optimization in computing systems.
Innovation Solution
A method for critical path analysis in computing environments that records and processes trace files by identifying time spans and activities, focusing on non-overlapping activities to determine the critical path, allowing for efficient memory usage and accurate performance evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional performance analysis tools record and analyze trace data to identify critical paths, then performance analysis capability is improved, but memory usage increases excessively especially for large traces
Solution Approach 1:
The patent extracts and processes only the essential information needed for critical path identification from the trace data. By focusing on activity start/end times and filtering out redundant information, the system achieves accurate performance analysis without loading entire trace files into memory simultaneously.
Solution Approach 2:
The trace analysis process is divided into sequential segments: loading trace data, identifying activities, determining time spans, filtering overlapping activities, and identifying critical paths. This segmentation allows processing of large traces in manageable portions rather than requiring all data in memory at once.
2Measurement precision
If conventional tools attempt to analyze all activities in trace data, then comprehensive analysis is achieved, but inability to isolate critical path activities reduces analysis effectiveness
Solution Approach 1:
The system dynamically identifies critical paths by comparing activity time spans and detecting overlaps in real-time during analysis. This dynamic approach automatically adapts to different trace scenarios and identifies the critical path without requiring manual configuration or intervention.
Solution Approach 2:
The analysis process uses feedback from activity overlap detection to iteratively refine the critical path identification. By checking whether activities overlap with previously identified critical path activities, the system continuously improves the accuracy of critical path isolation.
Data Source
AI summary
Embodiments for providing critical path analysis of active trace files in a cloud computing environment. A critical path may be identified using a trace of time spans and activities of a plurality of applications, wherein the critical path is a set of activities having time spans free of overlap with other activities.