Intelligent Dynamic Preloading for Web Caching

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Solution Overview

Problem

Current web content delivery systems face inefficiencies in rendering webpages, particularly for large sites with many pages, as they either incur performance penalties with on-demand rendering or require lengthy build steps for static rendering, which can be computationally burdensome and delay deployments.

Innovation Solution

Implementing intelligent dynamic preloading, where a prediction model generates a list of expected page requests and simulates these requests to a caching layer, allowing the caching layer to pre-render and store pages, thereby serving subsequent requests directly from cache without needing the host system to re-render pages.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If on-demand rendering is used for webpages, then the host system can handle dynamic content flexibly, but the device experiences performance penalties due to real-time rendering requirements

Engineering Contradiction:

Improveflexibility in handling dynamic contentVSAvoidwebpage delivery speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system performs preliminary rendering of webpages in advance based on predicted user requests. The origin server pre-renders pages and stores them in the CDN caching layer before actual user requests arrive, eliminating real-time rendering delays while maintaining adaptability through prediction-based selection

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The CDN caching layer acts as an intermediary between the origin server and client devices. It stores pre-rendered webpage versions and serves them directly to users, mediating the interaction between the host system and users to avoid direct real-time rendering demands on the origin server

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If static rendering with build steps is used, then webpage delivery speed is improved, but deployment time increases due to lengthy build steps

Engineering Contradiction:

Improvewebpage delivery speedVSAvoiddeployment time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the rendering strategy by using a prediction model to determine which pages to pre-render. Unlike static rendering of all pages, this dynamic approach adapts to actual usage patterns, pre-rendering only the most likely requested pages while allowing rapid deployment of new content

Inventive Principle:

Principle #15Dynamics

3Productivity

If all pages are pre-rendered and stored in cache, then subsequent requests are served quickly, but bandwidth and storage resources are consumed

Engineering Contradiction:

Improverequest serving speedVSAvoidbandwidth and storage resource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

Instead of pre-rendering all pages, the system applies partial action by pre-rendering only a subset of pages predicted to be requested. The prediction model identifies the most likely requested pages, and only those are pre-rendered and stored in the CDN cache, reducing unnecessary bandwidth and storage consumption while maintaining fast serving for predicted requests

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes the parameter of page selection from static (all pages or fixed set) to dynamic (prediction-based selection). By using the prediction model to determine which pages to pre-render, the system adapts to changing usage patterns and optimizes resource allocation based on actual demand predictions

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11783002B2Intelligent dynamic preloading

Publication Date: 2023.10.10 DRNC HOLDINGS INC

AI summary

Intelligent dynamic preloading is described. A list of page requests is generated using a prediction model that predicts a plurality of page requests for a site or web application that are expected to be received from client devices, where at least some of the page requests include URLs and one or more other parameters that affect generation of a corresponding page. A request is simulated for each of the page requests included on the list of page requests to a caching layer to cause the caching layer to: make a request to an origin server for the site or dynamic web application, receive rendered pages from the origin server, and store the received rendered pages, such that subsequent requests received at the caching layer can be served from the already rendered pages.