Performance Variability Detection Using Differential Graphs and LLMs
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Solution Overview
Problem
Manually reviewing extensive and detailed performance traces to identify sources of variability is tedious, computationally intensive, and prone to errors, often obscuring critical data and consuming valuable computing resources.
Innovation Solution
Utilize a large language model (LLM) to analyze focused, relevant performance data, generated by identifying commonalities and differences in control and treatment environments through differential graphs and matrices, to efficiently pinpoint sources of variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of extensive performance traces is performed, then detailed performance analysis can be conducted, but time consumption and computational overhead increase significantly
Solution Approach 1:
The patent extracts only the most relevant performance data from extensive traces by using differential graphs to compare control and treatment environments. This extraction process identifies and isolates critical performance variability sources, eliminating the need to manually review every detail while maintaining analysis accuracy.
Solution Approach 2:
The patent introduces differential graphs as an intermediary representation that translates complex performance traces into comparable formats. This intermediary structure enables automated comparison between control and treatment environments, reducing manual analysis time while preserving essential performance insights.
2Reliability
If comprehensive performance traces are reviewed to identify variability sources, then accurate performance diagnosis is achieved, but computational resources are consumed excessively
Solution Approach 1:
The patent extracts only essential performance characteristics needed for variability detection, filtering out redundant information. By extracting only the necessary data points for differential analysis, the system maintains diagnostic accuracy while significantly reducing computational resource requirements.
Solution Approach 2:
The patent transforms performance data into parameterized representations (differential graphs) that highlight variability sources. This parameter transformation consolidates complex trace data into key metrics, enabling accurate performance diagnosis with reduced computational overhead.
3Loss of information
If detailed performance traces containing numerous details are analyzed, then complete performance information is obtained, but irrelevant and duplicative information increases complexity
Solution Approach 1:
The patent extracts only performance information relevant to variability detection by comparing control and treatment environments. This selective extraction maintains complete performance information for analysis while filtering out irrelevant and duplicative details that increase complexity.
Solution Approach 2:
The patent segments performance traces into distinct control and treatment environment components, enabling targeted analysis of variability sources. This segmentation organizes complex trace data into manageable segments, reducing overall complexity while preserving essential information.
Data Source
AI summary
Methods, computer systems, and computer storage media are provided for identifying a source(s) of performance variability using relevant performance data. In embodiments, performance data indicating performance of a computing system is obtained. Such performance data is analyzed to identify relevant performance data including a representation of a differential graph that compares a first set of performance data associated with a first environment with a second set of performance data associated with second environment. Thereafter, a prompt is generated that includes the representation of the differential graph and a request for an identification of a source of performance variability associated with the relevant performance data. Based on the prompt, the source of performance variability associated with the relevant performance data may be identified via a large language model and provided for display.