SDN Controller Content Caching Optimization via BPSO
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Solution Overview
Problem
In information-centric networking (ICN) environments, content popularity estimation is inaccurate and biased, leading to degraded user quality of experience (QoE) due to the lack of consideration for content characteristics, especially in edge networks.
Innovation Solution
A content caching optimization system and method that predicts content requests using time-series prediction machine learning models, calculates content popularity, and performs caching optimization based on both popularity and preset content priority, utilizing a binary particle swarm optimization (BPSO) algorithm to minimize content transmission costs while ensuring quality of experience constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If content caching is performed based solely on content popularity in edge networks, then caching simplicity is improved, but content popularity estimation accuracy deteriorates due to biased information
Solution Approach 1:
The patent introduces an SDN controller as an intermediary between edge networks and core network. The SDN controller aggregates content request information from multiple edge networks to calculate comprehensive content popularity, eliminating the bias present in individual edge network data. This mediator enables accurate popularity estimation without complicating the caching operation at edge nodes.
Solution Approach 2:
The system separates the caching decision-making process into two independent parts: edge networks continue to perform simple popularity-based caching locally, while the SDN controller performs separate aggregation and optimization calculations. This segmentation allows each component to specialize - edge networks maintain simplicity while the central controller ensures accuracy.
2Device complexity
If all cached content is treated equally without considering content characteristics, then system complexity is reduced, but user quality of experience deteriorates
Solution Approach 1:
The patent applies different caching strategies to different content based on their characteristics and priorities. High-priority content receives preferential treatment in caching decisions, while low-priority content follows standard popularity-based caching. This local differentiation improves QoE without requiring complete system redesign.
Solution Approach 2:
The system modifies the caching optimization parameters to include content priority as an additional dimension. The SDN controller calculates optimal caching states by considering both content popularity and content priority parameters, enabling differentiated treatment of content while maintaining manageable system complexity through mathematical optimization.
3Reliability
If content caching optimization considers both content popularity and content priority, then user quality of experience is improved, but calculation complexity increases
Solution Approach 1:
The patent replaces complex manual or rule-based optimization mechanisms with a mathematical optimization model solved by the SDN controller. The binary particle swarm optimization algorithm automatically calculates optimal caching states by considering multiple parameters simultaneously, reducing the need for complex manual configuration and decision-making processes.
Data Source
AI summary
Provided are a content caching optimization system and method. The content caching optimization system in which content priority in an information-centric networking (ICN) environment is taken into consideration includes one or more producer terminals configured to generate and provide content, one or more user terminals configured to transmit content requests according to users and receive content according to the content requests, one or more mobilelmultiple access edge cotnputings (MECs) configured to predict the number of requests for each piece of content to be requested later on the basis of the content requests received from the user terminals, and a software-defined network (SDN) controller configured to calculate a content popularity using the number of requests for each piece of content predicted by the MECs and perform content caching optimization on the basis of the calculated content popularity and a preset content priority.