SRIF Nodes for Mobile Network Data Retention
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Solution Overview
Problem
Future mobile communication networks face challenges in retaining critical data due to limited storage capacity and the need for intelligent data processing without inducing latency, as they handle vast volumes of data from IoT devices and machine-type communications.
Innovation Solution
The implementation of a Storage and Retention Intelligence function (SRIF) system that offloads processor-intensive processes from network nodes, allowing them to focus on core functions like data routing, while the SRIF system manages data retention and extraction of intelligence using a hierarchical structure of central, middle layer, and end nodes with machine learning capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network nodes perform processor-intensive data processing operations, then data retention and intelligence extraction improve, but processing latency increases
Solution Approach 1:
The system segments data processing operations into two distinct parts: real-time data routing functions performed by network nodes, and processor-intensive data retention and intelligence extraction operations performed by cloud-based data lakes. This segmentation allows critical time-sensitive routing to occur at the edge while computationally intensive analysis occurs in the cloud, resolving the contradiction between retention reliability and processing latency.
Solution Approach 2:
The patent introduces an intermediary architecture where network nodes communicate with cloud-based data lakes through standardized interfaces. The data lakes act as intermediaries that receive, store, and process data without requiring network nodes to perform intensive processing locally. This intermediary layer enables reliable data retention while maintaining low-latency routing through the network nodes.
2Reliability
If network nodes retain all access data, then data availability improves, but storage capacity requirements increase beyond limits
Solution Approach 1:
The patent extracts the storage function from network nodes and relocates it to cloud-based data lakes. Network nodes continue to maintain data availability by routing and forwarding data in real-time, while the actual storage of vast volumes of access data occurs in the cloud. This extraction resolves the contradiction by providing unlimited storage capacity without requiring network nodes to exceed their storage limits.
Solution Approach 2:
The system transitions storage from a local dimension (network node storage) to a cloud dimension (data lake storage). This dimensional change allows the network to access virtually unlimited storage capacity in the cloud while network nodes maintain their primary function of real-time data routing. Data availability is preserved through the cloud interface while storage requirements are effectively unlimited.
3Productivity
If network nodes focus on core functions like data routing, then processing speed improves, but data retention capability decreases
Solution Approach 1:
The patent segments functionality between network nodes and cloud data lakes: network nodes specialize in high-speed data routing and forwarding (core functions), while cloud data lakes specialize in comprehensive data retention and intelligence extraction. This functional segmentation enables network nodes to achieve maximum routing speed without being burdened by retention responsibilities, while the cloud system provides robust retention capability.
Solution Approach 2:
The cloud-based data lakes provide self-service data retention capabilities that supplement network node functionality. Network nodes perform their core routing functions efficiently, while the cloud system automatically performs data retention, storage, and analysis. This self-service approach allows network nodes to maintain high productivity without sacrificing retention capability, as the cloud system independently handles retention tasks.
Data Source
AI summary
In an embodiment, a computer implemented method and architecture for managing data in mobile communication network which includes core and access components. This embodiment performs specialized data handling through processing nodes referred as Storage Retention and Intelligent Function (SRIF) nodes, an evaluation operation on control plane and user plane data received from the mobile communication network. This action determines whether any portion of the data needs intelligent processing and applies knowledge extraction algorithm for build-up retention or policy decision. As responsive to the evaluation operation, the SRIF nodes apply decisions on data or enable network nodes to apply data processing rules. The architecture of SRIF is hierarchical comprising end node as serving node, middle node as load balancing node providing flexibility, and central node as the brain. The central node performs data processing based on pre-defined rules or algorithms developed by analyzing data or by applying Machine learning.