Intermediary Server Speculation Configuration for Bandwidth-Aware Prefetching
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Solution Overview
Problem
Existing methods for resource prefetching and prerendering, such as using the attribute and HTTP/2 server push, are laborious for developers and inefficient due to lack of insight into resource needs and result in wasted bandwidth and slowdowns.
Innovation Solution
An intermediary server uses a machine learning model to automatically generate speculation configurations for prefetching and prerendering resources based on client behavior patterns, reducing manual effort and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If developers manually specify prefetching attributes on each page, then resource prefetching can be controlled, but developer effort and complexity increase significantly
Solution Approach 1:
The system enables automatic speculation configuration where the server generates speculation rules autonomously based on analytics data about user behavior patterns. The server serves itself by automatically identifying which resources to prefetch without requiring developer intervention, thus resolving the contradiction between precise control and developer effort
Solution Approach 2:
The speculation configuration acts as an intermediary layer between the server and client, automatically generated by the server based on analytics data. This intermediary mechanism translates complex user behavior patterns into simple prefetching instructions, eliminating the need for developers to manually configure each prefetching rule while maintaining precise control
2Speed
If HTTP/2 server push is used to send resources before client requests, then resource loading speed improves, but bandwidth is wasted when resources are already loaded or not needed
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns through analytics data to predict which resources will be needed next. Speculation rules are generated in advance based on these predictions, allowing the server to push only the specific resources that are likely to be requested, avoiding the waste of pushing unnecessary resources
Solution Approach 2:
The system uses analytics data as feedback to continuously improve speculation accuracy. By monitoring actual user navigation patterns and comparing them with predictions, the server refines its speculation rules over time, ensuring that pushed resources are actually needed and reducing bandwidth waste from incorrect predictions
3Ease of operation
If speculation rules are defined by developers, then prefetching behavior can be controlled, but accuracy of resource prediction decreases due to lack of insight
Solution Approach 1:
The server acts as an intermediary that collects and analyzes analytics data to generate speculation rules. This intermediary process transforms raw user behavior data into accurate prediction insights that would be difficult for developers to derive manually, thereby improving prediction accuracy while maintaining ease of operation through automated rule generation
Solution Approach 2:
The server performs self-analysis of analytics data to automatically generate accurate speculation rules without requiring developer expertise in predicting user behavior patterns. The system serves its own prediction needs by leveraging its access to comprehensive analytics data, achieving high accuracy while keeping the operation simple for developers
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.