Web Page Hint Generation for Faster Object Prefetching
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Solution Overview
Problem
Existing web page loading processes are inefficient due to multiple request/response round-trip communications and phases associated with each object, leading to significant delays in user experience.
Innovation Solution
Implement server-machine-driven hint generation based on client-machine-driven feedback, where hinting processors generate and refine machine-learning-based models to optimize web page rendering by providing optimized fetching hints to client page renderers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multiple request/response round-trip communications are used to load web page objects, then complete web page content can be delivered, but web page loading time increases significantly
Solution Approach 1:
The hinting processor generates hinting information in advance that predicts which web objects will be needed and when. This preliminary action allows the client to prefetch objects before they are actually requested, eliminating the need for multiple sequential request/response round-trips and significantly reducing web page loading time.
Solution Approach 2:
The system implements a feedback mechanism where the client sends information about actually rendered objects back to the hinting processor. The hinting processor uses this feedback to refine and update its predictive models, continuously improving the accuracy of future predictions and optimizing object fetching efficiency over time.
2Measurement precision
If persistent communication between hinting processors and client computers is implemented to monitor behavior, then accurate prediction models can be developed, but system complexity and communication overhead increase
Solution Approach 1:
The content server acts as an intermediary that receives hinting requests from clients and retrieves hinting information from the hinting processor. This mediator architecture simplifies client-server communications by centralizing the hinting functionality on the server side, reducing the complexity of persistent client-processor connections while maintaining accurate prediction capabilities.
Solution Approach 2:
The hinting processor autonomously generates and updates hinting information based on received feedback without requiring persistent monitoring connections to individual clients. The system self-services by processing feedback batches and automatically refining prediction models, reducing communication overhead and architectural complexity.
Data Source
AI summary
Embodiments seek to improve web page loading time using server-machine-driven hint generation for based on client-machine-driven feedback. For example, client computers having page renderers are in communication with content servers and hinting processors. The hinting processors can use hinting feedback from multiple page rendering instances to automatically generate hints for optimizing loading and/or rendering of those pages. In some implementations, in response to page requests from the page renderers, content servers can request hints from hinting processors and send those hints to the requesting page renderers for use in improving the page loading experience. In other implementations, in response to page requests from the page renderers, content servers can instruct the requesting page renderers to contact an appropriate hinting processor and to retrieve appropriate hints therefrom for use in improving the page loading experience.