Speculation Configuration via Intermediary Server for Accurate Prefetching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for resource prefetching and prerendering in web browsing require manual developer intervention and lack accuracy, leading to inefficient use of bandwidth and resources due to incorrect predictions.
Innovation Solution
An intermediary server uses machine learning to automatically generate speculation configurations for prefetching and prerendering resources based on historical data and user behavior, allowing clients to dynamically manage when and what resources to fetch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual developer specification of prefetching resources is used, then resource prefetching can be implemented, but developer effort increases and accuracy decreases
Solution Approach 1:
The system enables automatic speculation configuration management where the intermediary server autonomously generates speculation configurations using machine learning models based on historical data and user behavior patterns, eliminating the need for manual developer intervention while improving prediction accuracy through data-driven insights
Solution Approach 2:
An intermediary server is introduced between the client and origin server to automatically generate and manage speculation configurations, serving as a mediator that translates historical data into actionable prefetching rules without requiring developer manual configuration
2Speed
If HTTP/2 server push is used to send resources before client requests, then resource availability improves, but bandwidth waste increases due to incorrect predictions
Solution Approach 1:
The system dynamically generates speculation configurations based on real-time analysis of historical data and user behavior patterns, allowing the resource prefetching strategy to adapt and optimize itself continuously, thereby improving resource availability while minimizing bandwidth waste through accurate, context-aware predictions
Solution Approach 2:
The machine learning model utilizes historical data and user behavior feedback to continuously refine speculation configurations, creating a closed-loop system that learns from past prefetching outcomes to improve future resource prediction accuracy and reduce bandwidth waste
3Adaptability or versatility
If speculation rules are manually defined by developers, then resource prefetching can be controlled, but system complexity increases and adaptability decreases
Solution Approach 1:
The intermediary server automatically generates and manages speculation configurations using machine learning algorithms that analyze historical data and user behavior, enabling the system to adapt to changing patterns without requiring manual configuration updates or complex developer intervention
Data Source
AI summary
Automatic speculation configuration management is described. An intermediary server receives a request from a client. The resource is retrieved from the origin server, where the resource includes link(s) to other resource(s). The intermediary server generates and transmits a response that includes a header that references a speculation configuration for prefetching at least one of the other resource(s). The intermediary server receives a request for the speculation configuration from the client. The intermediary server generates and transmits a response to the client that includes the speculation configuration. The intermediary server receives a prefetching request from the client for one of the resources indicated in the speculation configuration, retrieves that resource, and transmits a response to the client with that resource.