Web Page Load Time Prediction via Dependency Graph Simulation
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Solution Overview
Problem
Cloud service performance prediction is challenging due to the complexity of cloud service implementations, variability of web object dependencies, and the difficulty in assessing the impact of parameter modifications, leading to inefficient and error-prone manual trial-and-error methods for optimizing page load times.
Innovation Solution
Automated performance prediction techniques that simulate the modification of parameters affecting cloud service performance, such as round-trip time, network processing time, and client execution time, by extracting parental dependency graphs and simulating page loading scenarios to estimate new page load times and compare them to original times, enabling the identification of optimal parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual trial-and-error methods are used to optimize cloud service performance, then implementation simplicity is maintained, but time consumption and error rate increase significantly
Solution Approach 1:
The system performs self-service by automatically extracting dependency graphs, simulating parameter modifications, and predicting performance outcomes without requiring manual intervention. The cloud service system itself generates the optimization insights through automated analysis of its own web object dependencies and loading patterns.
Solution Approach 2:
The patent replaces manual mechanical trial-and-error optimization with an automated computational system that uses dependency graph extraction and simulation techniques. Instead of manually testing different parameter configurations, the system computationally models the effects of parameter changes on page load time based on extracted dependency relationships.
2Productivity
If hardware changes are implemented to improve cloud service performance, then performance gains are achieved, but implementation complexity and cost increase
Solution Approach 1:
The system focuses on optimizing software parameters (such as web object loading sequences, caching strategies, and resource allocation) rather than making hardware changes. By analyzing dependency graphs and simulating parameter modifications, the system identifies optimal configuration settings that improve performance without requiring complex hardware modifications or additional infrastructure investments.
3Measurement precision
If comprehensive performance analysis is performed to identify optimal parameters, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the complex web page into individual web objects and extracts their dependency relationships into a structured graph format. This segmentation allows the system to analyze dependencies between individual objects rather than treating the entire page as a monolithic unit, making the computational analysis more manageable while maintaining high prediction accuracy.
Solution Approach 2:
The patent introduces a dependency graph as an intermediary data structure that mediates between the raw web page content and the performance prediction analysis. This intermediary representation simplifies the complex relationships between web objects, making it easier to simulate parameter changes and predict their impact on overall page load time without requiring direct analysis of all underlying complexities.
Data Source
AI summary
Embodiments of automated cloud service performance prediction are disclosed. The automated cloud service performance prediction includes extracting one or more dependency relationships for each web object in the webpage. The prediction further includes determining an original performance metric value and original timing information associated with a page loading of a webpage. The prediction also includes simulating a page loading of the webpage based on the adjusted timing information and the dependency relationships to estimate a new performance metric value associated with the simulated page loading of the webpage. The prediction additionally includes comparing the original performance metric value to the new performance metric value to determine whether the adjusted timing information increased or decreased the new performance metric value relative to the original performance metric value.