Graph-Based Cloud App Performance Optimization
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Solution Overview
Problem
Optimizing application performance in cloud computing environments is challenging, especially for machine learning applications, due to difficulties in evaluating key data structures and algorithms, managing performance across varying conditions, and manually determining optimal configuration parameters.
Innovation Solution
An automated performance optimization system comprising a transaction observer, a classifier recorder and tagger, a graph engine relation builder, and a recommendation engine, which receives transaction information, tags and graphs it, and generates optimization recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual determination of configuration parameters is used, then optimization accuracy can be achieved, but time consumption and error rate increase substantially
Solution Approach 1:
The system enables self-service optimization by automatically collecting transaction information, tagging it with classifiers, building execution graphs, and generating optimization recommendations without human intervention. The platform serves itself by autonomously analyzing application performance data and producing actionable insights, eliminating the need for manual parameter determination while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of analyzing transaction data and determining configuration parameters with an automated computational system. Machine learning classifiers, graph processing engines, and recommendation algorithms substitute human analysts, dramatically reducing time consumption while preserving or improving optimization accuracy through consistent, error-free automated analysis.
2Measurement precision
If comprehensive transaction analysis is performed, then optimization recommendation quality improves, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct modular components: transaction information collection, classification and tagging, graph relationship building, and recommendation generation. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while enabling comprehensive transaction analysis through the coordinated work of specialized sub-systems.
Solution Approach 2:
The patent introduces intermediary components such as the classifier recorder and graph engine that mediate between raw transaction data and final recommendations. These intermediaries transform and structure data in manageable formats, bridging the gap between comprehensive data collection and actionable insights, thereby improving recommendation quality without proportionally increasing system complexity.
3Productivity
If automated tagging and graph building is implemented, then analysis efficiency improves, but processing overhead increases
Solution Approach 1:
The system performs preliminary actions by tagging transaction information with classifiers before graph building occurs. This pre-processing step organizes data into structured formats with meaningful labels, making subsequent graph construction and analysis more efficient. The preliminary classification reduces the computational burden during graph building and recommendation generation, improving overall analysis efficiency while managing processing overhead through staged computation.
Data Source
AI summary
Some embodiments are associated with application performance optimization in a cloud computing environment. A transaction observer platform may receive transaction information associated with execution of an application in the cloud computing environment A classifier recorder and tagger platform, coupled to the transaction observer platform, may then automatically tag the transaction information. A graph engine relation builder platform, coupled to the transaction observer platform and the classifier recorder and tagger platform, may receive the tagged transaction information and automatically create graph information that represents execution of the application. A recommendation engine platform, coupled to the graph engine relation builder platform, may then receive the graph information and automatically generate and transmit an application performance optimization recommendation.