Cache Decision Method for 5G MEC Network Load Reduction
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Solution Overview
Problem
The 5G network faces challenges in improving cache hit rates, leading to increased transmission latency and network load, as existing solutions lack effective methods for determining cache content deployment in mobile edge computing architectures.
Innovation Solution
A cache decision method and apparatus that utilize network data analytics to collect and analyze historical user access data, combining local and network-wide decisions to determine optimal cache content, thereby improving cache hit rates and reducing network load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If content is cached at a location close to the user to reduce transmission latency, then transmission latency is reduced, but cache hit rate is low due to ineffective cache decision methods
Solution Approach 1:
The system performs preliminary cache decisions by analyzing historical user access data and predicting future access patterns. The cache decision function determines which content should be pre-cached at edge locations before users actually request it, based on statistical analysis of access frequencies and user behavior patterns. This preliminary action ensures that relevant content is already available at the edge when users need it.
Solution Approach 2:
The system implements a feedback mechanism where the cache decision function continuously receives information about actual user access patterns and cache performance. Based on this feedback, the system adjusts and optimizes cache deployment decisions, refining its predictions of which content will be accessed and improving cache hit rates over time through iterative learning.
2Reliability
If cache content is increased to improve cache hit rate, then cache hit rate is improved, but network load and transmission bandwidth increase
Solution Approach 1:
The system applies local quality by deploying different cache content at different edge locations based on local user access patterns. Instead of uniformly caching the same content everywhere, the cache decision function analyzes local statistics and deploys content that is most relevant to each specific location, maximizing cache effectiveness while minimizing redundant content storage and network load.
Solution Approach 2:
The system uses partial action by caching only the most frequently accessed content at each edge location rather than caching all possible content. The cache decision function identifies and caches only the essential portion of content that will actually be used, avoiding the network load associated with transmitting and storing excessive content that would never be accessed.
3Reliability
If statistical information is collected and analyzed to make cache decisions, then cache hit rate is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the cache decision process into distinct functional components: a statistics collection function that gathers access data, a cache decision function that analyzes the data and makes deployment decisions, and a content delivery function that executes the decisions. This segmentation allows each component to be optimized independently and simplifies the overall complexity by dividing the data processing task into manageable stages.
Solution Approach 2:
The cache decision function serves as an intermediary between raw statistical information and cache deployment actions. It processes and interprets statistical data, transforming it into actionable cache deployment decisions. This intermediary layer simplifies the system by providing a dedicated component that handles the complexity of data analysis, isolating it from both the data collection and execution components.
Data Source
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AI summary
This application provides a cache decision method and apparatus. The method includes: receiving, by a policy control function PCF, first cache information sent by at least one local analytic function LAF, where the first cache information includes a base station identifier, identifiers of first N1 pieces of application information that are accessed for a maximum quantity of times, and content corresponding to the identifiers of the N1 pieces of application information, and N1 is a positive integer; and determining, by the PCF, local cache content based on the first cache information and a capacity of a local cache. In this way, a cache hit rate can be effectively improved, transmission bandwidth and data transmission overheads are greatly reduced, and users' experience of using the network is improved.