Selective CDN Pre-warming via User Action Prediction
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Solution Overview
Problem
Existing content delivery networks (CDNs) face challenges in efficiently pre-caching content objects to meet user timeliness expectations, as widespread caching techniques are often prohibitively expensive and inefficient.
Innovation Solution
A method and system for automatically identifying and pre-caching content objects in CDNs by analyzing user actions and content categorizations, predicting request probabilities, and geographically optimizing caching based on access behaviors and content-group structures to improve access speed for frequently requested content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If content objects are cached at locations near users using high-speed resources, then access speed is improved, but cost increases prohibitively
Solution Approach 1:
The patent applies local quality by differentiating caching strategies based on content characteristics and user behavior patterns. Instead of uniformly caching all content at all edge locations, the system identifies specific content objects that benefit from pre-caching based on their access patterns, categories, and user preferences, thereby optimizing storage resource allocation while improving access speed for relevant content
Solution Approach 2:
The system performs preliminary action by proactively pre-caching content objects at edge servers before users actually request them. The content request function analyzes tracked requests and predicts future content needs, then pre-positions content at geographically distributed edge servers, so that when users request content, it is already available locally rather than needing to be retrieved from remote origin servers
2Reliability
If widespread caching is implemented to meet user timeliness expectations, then service quality is improved, but cost becomes prohibitively expensive
Solution Approach 1:
The patent applies partial action by implementing caching for only the subset of content objects that demonstrate characteristics indicating future user requests. The content request function selectively identifies content for pre-caching based on tracked request patterns, content categories, and user behavior analysis, rather than caching all content. This partial caching approach maintains service quality for predicted content needs while avoiding the prohibitive costs of widespread caching of all content
3Quantity of substance
If content is pre-cached based on user actions and content categorizations, then storage resources are optimized, but system complexity increases
Solution Approach 1:
The system applies self-service by implementing an automated content request function that autonomously tracks user requests, analyzes content categories, identifies caching opportunities, and pre-positions content at edge servers without requiring manual intervention. The system self-manages the complexity of analyzing user behavior patterns and making caching decisions, thereby optimizing storage resource allocation while containing system complexity through automation rather than manual processes
Data Source
AI summary
Systems and methods are provided for streaming content over the Internet via a CDN to an end user system. Requests from end user systems for streaming content objects being handled by the CDN are tracked to identify, for each request the tracked requests, a first content object being requested. For each request of the tracked requests, a category of the requested first content object is identified. The identified category is stored. Aggregated data indicating an amount of requests being for content objects with a specific category is generated. A prediction is made that a second content object will be requested in the future based on the aggregated data and a category of the second content object. Access to the second content object is improved such that the second content object is set to be provided faster in response to a request for the second content object than would otherwise occur.