Caching Server User Profile Correlation Analysis
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Solution Overview
Problem
Current caching systems face inefficiencies in predicting content to cache, leading to increased cache misses and network congestion, as they lack effective methods to analyze user behavior and correlation between users.
Innovation Solution
A method and system that determine user profiles, obtain correlation measurements, and calculate content caching priorities based on user profiles and content history, utilizing a central correlation measurement provider server and dynamic data sources to enhance caching accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional caching algorithms are used to determine content to cache, then cache storage capacity is managed, but caching accuracy decreases leading to more cache misses
Solution Approach 1:
The system implements feedback loops by continuously monitoring user behavior patterns, access frequencies, and cache performance metrics. Correlation measurements are regularly updated based on new data, and caching decisions are adjusted according to feedback from actual cache hit/miss outcomes, enabling continuous improvement of caching accuracy
Solution Approach 2:
The patent replaces traditional mechanical caching algorithms (which rely on simple rules like LRU - Least Recently Used) with an intelligent system that uses correlation measurements and user profile analysis. This substitution enables the system to predict content requests more accurately by understanding user behavior patterns rather than just following mechanical access patterns
2Reliability
If user behavior analysis is implemented to improve caching predictions, then caching accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of caching prediction into distinct components: user profile creation, correlation measurement calculation, content history analysis, and caching decision generation. Each component handles a specific aspect of the prediction process, making the overall system more manageable and maintainable despite its complexity
Solution Approach 2:
The patent introduces intermediary elements such as user profiles and correlation measurements that mediate between raw user behavior data and caching decisions. These intermediaries simplify the decision-making process by pre-processing and structuring information, reducing the complexity of the final caching algorithm
3Measurement precision
If correlation measurements are calculated on demand, then data freshness and accuracy improve, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing user profiles and correlation measurements before they are needed for caching decisions. This allows the caching system to quickly retrieve pre-computed data rather than calculating correlations in real-time, reducing processing time while maintaining data freshness through periodic updates
Data Source
Figure 1a
Figure 1b~1c
Figure 2~3
AI summary
It presented a method comprising the steps of: determining, in a caching server of a telecommunication network, a user profile to analyse; obtaining, in the caching server, a group of user profiles; obtaining correlation measurements for each user profile in the group of user profiles in relation to the user profile to analyse; and calculating a content caching priority for at least one piece of content of a content history associated with the group of user profiles, taking the correlation measurement into account. A corresponding server, computer program and computer program product are also provided.