CDN Content Caching Prioritization via Hit Time Estimation
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Solution Overview
Problem
Content distribution networks face challenges in optimizing cache hits to reduce delays and computational load, as existing methods do not effectively prioritize content caching based on user behavior and geographical proximity.
Innovation Solution
A system that uses hit times estimation logic to determine a prioritized list of contents for caching in content distribution clusters, predicting user preferences and incorporating social network information to maximize cache hits, thereby optimizing content delivery by redirecting requests to geographically closer servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If content is cached in content distribution clusters, then access delays are reduced, but cache hit rate decreases without effective prioritization
Solution Approach 1:
The system performs preliminary actions by predicting future content requests using collaborative filtering algorithms and social network information before requests actually occur. Content is proactively cached in distribution clusters based on predicted popularity and user preferences, ensuring high cache hit rates when requests arrive while minimizing access delays.
Solution Approach 2:
The patent replaces traditional mechanical caching mechanisms (which simply store recently accessed content) with an intelligent prediction system using collaborative filtering and social network analysis. This substitution enables the system to anticipate content demand patterns and pre-position content optimally across the CDN infrastructure.
2Reliability
If more content is cached in content distribution clusters, then cache hit rate improves, but computational load increases
Solution Approach 1:
The system applies local quality by tailoring content caching strategies to specific geographical locations and user communities. Each content distribution cluster receives customized content recommendations based on local user preferences, social network structures, and regional content popularity patterns. This localized approach maximizes cache hit rates in each region while avoiding the computational overhead of centralized content management for the entire CDN.
Solution Approach 2:
The patent implements partial action by selectively caching only the most predicted-popular content items in each distribution cluster rather than caching all possible content. The system calculates predicted hit probabilities and prioritizes caching resources on content with the highest expected demand, achieving high cache hit rates with optimized computational resource utilization.
3Ease of manufacture
If content prioritization is based on traditional methods, then implementation is simple, but user preference accuracy decreases
Solution Approach 1:
The system introduces social network information as an intermediary layer between user behavior data and content prioritization decisions. By analyzing social connections, friendships, and community patterns, the system indirectly infers user preferences and content popularity trends. This intermediary approach captures collective wisdom and emerging trends that traditional individual-based methods miss, significantly improving prediction accuracy while maintaining implementation feasibility through modular algorithm integration.
Data Source
AI summary
A device includes a processor. The processor is configured to determine a hit time for each of a plurality of content items based on at least one of users' history of access to the content items on content distribution clusters in a content distribution network, the users' past ratings of the content items, and social network information associated with the users. The hit time of a content item indicates a number of times that the content item is likely to be accessed by the users. The processor is further configured to compute caching priorities of the content items based on a caching policy of the device and the determined hit times, and initiate a redistribution, over a network, of the plurality of content items over the content distribution clusters of the content distribution network based on the caching priorities.