Edge Node Cache Data Distribution for 5G Latency
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Solution Overview
Problem
Existing 5G edge networks lack a mechanism to optimally manage cache data and user requests over edge nodes, as they fail to adapt to dynamic user density and association patterns, leading to inefficiencies in content caching, especially during high user request scenarios.
Innovation Solution
A method and system that dynamically manage cache data by receiving node parameters, MEC node association metrics, and data request parameters, identifying variations, and selecting the most suitable edge node based on predefined thresholds to store cache data, ensuring optimal caching even when the nearest edge node is not the best option.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If cache data is stored at the nearest edge node, then access speed is improved, but network congestion and latency increase during high user request scenarios
Solution Approach 1:
The system segments user requests and cache data across multiple edge nodes rather than concentrating them at a single nearest node. The orchestration module distributes cache data to different edge nodes based on user density and location affinity, dividing the load to prevent bandwidth congestion while maintaining fast access through localized caching.
Solution Approach 2:
The system adds dimensional considerations beyond simple proximity by incorporating user density and location affinity metrics into the caching decision. Instead of only considering physical distance to the edge node, the system evaluates multiple dimensions including user distribution patterns and node capacity, selecting optimal edge nodes that balance access speed with network load distribution.
2Productivity
If cache data is distributed across multiple edge nodes, then bandwidth utilization is optimized, but system complexity increases
Solution Approach 1:
The system introduces an orchestration module as an intermediary that manages cache data distribution across edge nodes. This central coordination layer handles the complexity of selecting optimal edge nodes based on user density and location affinity, while individual edge nodes simply execute caching operations according to orchestration decisions, thereby distributing productivity benefits without proportionally increasing complexity at each node.
Solution Approach 2:
The system dynamically changes parameters such as user density thresholds and location affinity weights to optimize cache distribution. By adjusting these parameters based on real-time network conditions and user patterns, the system adapts its caching strategy to maximize bandwidth utilization while maintaining manageable complexity through standardized decision criteria.
3Adaptability or versatility
If edge nodes dynamically adapt to user density and location affinity, then caching efficiency is improved, but computational overhead increases
Solution Approach 1:
The system performs preliminary analysis of user density and location affinity patterns to pre-determine optimal edge nodes for cache data placement. By proactively identifying and preparing optimal caching locations before user requests arrive, the system reduces real-time computational overhead while maintaining high caching efficiency through pre-computed location affinity metrics and user density assessments.
Data Source
AI summary
This disclosure relates to system and method for managing cache data in a network through edge nodes. The method includes receiving set of first node parameters, set of Mobile Edge Computing (MEC) node association metrics, and set of data request parameters from the user device; identifying a variation in at least one of the set of first node parameters, the set of MEC node association metrics, and the set of data request parameters; selecting a second edge node from the plurality of edge nodes to store the cache data based on a set of second node parameters, the set of MEC node association metrics, and the set of data request parameters, when variation is above predefined threshold; associating the second edge node with the user device to store the cache data for the user device; and dynamically managing cache data for the user device in the second edge node.