Machine-Generated Time-Dependent Hints for Web Prefetching
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Solution Overview
Problem
Existing web page loading techniques do not effectively utilize time-dependent information to optimize prefetching, leading to inefficiencies in loading times due to resource management and network latency.
Innovation Solution
Implementing time-dependent hint generation based on machine-generated probabilities derived from resource samples collected over time, allowing clients to generate their own hints even when external systems are unavailable, optimizing prefetching by predicting resource needs based on historical usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional prefetching without time-dependent information is used, then the system is simpler to implement, but web page loading times are longer due to inefficiencies in resource management and network latency
Solution Approach 1:
The system performs preliminary actions by collecting resource samples over time and computing time-based probabilities before actual web page loading occurs. This allows the system to predict which resources will be needed and prefetch them in advance, reducing loading time without requiring complex real-time decision-making during page load
Solution Approach 2:
Client machines are equipped with the capability to generate their own time-dependent hints using locally stored time-based probabilities. This self-service approach eliminates the need for complex external hinting services during page loading, reducing system complexity while maintaining improved loading performance
2Productivity
If time-dependent hint generation is implemented, then prefetching is optimized by predicting resource needs, but the system requires collecting and processing resource samples over time
Solution Approach 1:
The system performs preliminary data collection by gathering resource samples over an extended period before actual use. Time-based probabilities are computed in advance from this historical data, enabling efficient prefetching decisions without requiring complex real-time analysis during web page loading
Solution Approach 2:
The system creates simplified copies of resource usage patterns through time-based probabilities. Instead of processing raw resource sample data during prefetching decisions, the system uses these probabilistic models that capture essential usage patterns, significantly reducing processing complexity while maintaining prefetching effectiveness
3Reliability
If external hinting systems are always required, then centralized control is maintained, but client machines cannot generate hints when external systems are unavailable
Solution Approach 1:
Client machines are equipped with self-service capability to generate time-dependent hints using locally stored time-based probabilities. This allows the system to maintain reliability during external system unavailability while reducing adaptability concerns through the optional nature of external hinting services
Solution Approach 2:
The system prepares contingency plans by storing time-based probabilities and hint generation capabilities locally on client machines before external systems become unavailable. This beforehand cushioning ensures continuous operation during external system failures without requiring complex dynamic adaptation
Data Source
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AI summary
Embodiments seek to improve prefetch hinting using time-dependent, machine-generated hints. Some embodiments operate in context of client machines in communication hinting machines that can develop information about whether and how resources are used in network transactions over time by collecting "resource samples." Each resource sample can identify rendering status information of a resource at a sample time. The time-based samples can be used to compute time-based probabilities for the resources, indicating, for example, the likelihood of a resource being used to render a web page at some subsequent time. Time-dependent hints can be generated as a function of the time-based probabilities, and the time-dependent hints can be used to improve prefetching by optimizing the hinting information with respect to a particular request time (e.g., the prefetching hints for rendering a web page can be generated in a manner that accounts for when the web page is being rendered).