Multi-Page Resource Prefetch via Hierarchical Transition Analysis
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Solution Overview
Problem
Predicting webpage transitions to prefetch resources is challenging due to changing URLs across users or requests, leading to inefficient bandwidth usage and the need for improved prefetching techniques.
Innovation Solution
The Multi-Page Resource Prefetch (MRPP) technique infers high-probability resources for prefetching by analyzing the hierarchical structure of webpages, without requiring code changes or direct access to webpages, using a virtualization client to collect and analyze resource data across multiple user sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If webpage transition prediction is used to prefetch resources, then loading speed is improved, but bandwidth is wasted due to incorrect predictions
Solution Approach 1:
The system performs preliminary resource prefetching by analyzing historical webpage transition patterns and resource dependencies. It proactively loads resources into cache before they are actually needed, based on predicted user navigation behavior, thereby reducing loading time while minimizing unnecessary bandwidth consumption through intelligent prediction algorithms
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual user navigation patterns and comparing them with predicted transitions. This feedback loop enables the system to learn from incorrect predictions and improve its accuracy over time, reducing bandwidth waste while maintaining fast loading speeds
2Loss of time
If webpage transition prediction is used to prefetch resources, then loading time is reduced, but prediction accuracy deteriorates due to changing URLs
Solution Approach 1:
The system addresses changing URLs by transforming the prediction problem from URL-based to resource-based. Instead of predicting specific URL transitions, it analyzes resource dependency patterns and transition probabilities across different URL structures, making the prediction model robust to URL changes while maintaining accurate loading time optimization
Solution Approach 2:
The system creates a universal prediction model that works across multiple URL structures and webpage formats. By focusing on common resource types and dependency patterns rather than specific URLs, the model achieves broad applicability and maintains prediction accuracy despite variations in URL schemes across different users and requests
3Loss of time
If resource prefetching is implemented, then network latency is reduced, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including a resource analysis module, transition probability calculator, and cache management system. These intermediaries break down the complex prefetching task into manageable sub-tasks, analyzing resource dependencies, calculating transition probabilities, and managing cache operations separately, thereby reducing overall system complexity while achieving low latency
Data Source
AI summary
A plurality of sets of information is received, wherein each set of information indicates dependent resources of a corresponding referred web content, wherein each set of information is collected in response to the corresponding referred web content having been loaded on one of one or more client devices. The received sets of information associated with the corresponding referred web content are used to update a list of candidate prefetchable resources for a referring web content that refers to the corresponding referred web content. A request requesting for a list of prefetchable resources is received from a requestor, wherein the request is associated with loading the referring web content on the requestor's client device. One or more resources from the list of candidate prefetchable resources are selected as prefetchable resources based on one or more criteria. One or more identifiers identifying the one or more selected resources are sent.