Client-Side Web Page Caching Using Probability Data
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Solution Overview
Problem
Online merchant Web pages often experience delays in loading due to network bandwidth and device limitations, such as in portable devices, which hinder efficient delivery and display of e-commerce site content.
Innovation Solution
Implementing a client-side caching system that uses probability data to prioritize caching of Web pages based on historical visitor behavior, allowing for asynchronous caching of high-probability pages, thereby reducing reliance on server retrieval and improving loading times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If Web pages are transmitted only in response to customer requests, then network bandwidth is conserved, but loading delays increase
Solution Approach 1:
The system pre-loads and caches Web pages at the client device before they are actually requested by the customer. The server proactively determines which pages to cache based on various factors, and these pages are stored locally on the customer's device in advance, so when the customer navigates to them, they load immediately without network delay.
Solution Approach 2:
The caching strategy is dynamically adjusted based on multiple changing factors including customer browsing behavior, network conditions, device capabilities, and product availability. The system continuously adapts which pages to cache and how aggressively to cache them, optimizing the balance between bandwidth usage and loading speed in real-time.
2Speed
If more data is cached at the client device, then loading speed improves, but device storage and processing requirements increase
Solution Approach 1:
The system tailors the caching strategy to the specific characteristics of each customer's device. It assesses device capabilities such as storage capacity, processing power, and memory availability, then adjusts the amount and type of data cached accordingly. This ensures optimal performance without overwhelming the device's resources.
Solution Approach 2:
The system changes key parameters of the caching operation based on device capabilities and network conditions, including cache size limits, priority levels for different pages, and refresh intervals. These parameters are dynamically adjusted to match the specific constraints and opportunities presented by each customer's environment.
3Ease of operation
If Web pages are pre-cached based on probability data, then customer experience improves, but server processing overhead increases
Solution Approach 1:
The system uses automated algorithms and machine learning models that operate autonomously to determine which pages to cache, when to cache them, and how to manage cache content. This self-service approach minimizes manual intervention and reduces the perceived processing overhead by delegating decisions to intelligent systems that learn from patterns in customer behavior.
Data Source
AI summary
A merchant system computes various probabilities that visitors to a Web site will request individual Web pages of the Web site. The computed probabilities are then utilized to cache Web pages having the highest probabilities of being requested to a client device. The probability data may include aggregate probability data that defines the probability that any visitor to the Web site will request a Web page, customer segment probability data that defines the probability that customers in a particular customer segment will visit the Web pages, and/or customer-specific probability data that defines the probability that a specific customer of the Web site will visit the Web pages. Only Web pages having a computed probability greater than a caching threshold may be cached at the client device. Additionally, the Web pages may also be cached at the client device based upon the actual interaction with the Web site by a visitor.