Distributed Data Mesh Architecture for Low-Latency 5G Data Management
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Solution Overview
Problem
Mobile networks face challenges in managing vast amounts of data generated across network infrastructure components due to data loss, complexity, and inefficiencies in data management, particularly exacerbated by 5G networks, leading to issues like data redundancy, latency, and resource wastage.
Innovation Solution
A distributed data mesh system that stores and manages data close to its source, using microservices and data domains to ensure data integrity and accessibility, with a data catalog for efficient data management and governance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is centralized in remote data pools, then data management becomes simplified, but data loss increases and latency increases
Solution Approach 1:
The patent divides the centralized data pool into multiple distributed data pools, with each data pool located near its data source. This segmentation allows data to remain localized while still enabling centralized management through the data mesh architecture, thereby reducing data loss without sacrificing management simplicity.
Solution Approach 2:
The patent introduces a new architectural dimension by implementing a data mesh that operates across multiple layers - local data pools at the edge and a centralized data catalog at the core. This dimensional transformation enables simultaneous local data retention and global data management, resolving the contradiction between simplification and data loss prevention.
2Device complexity
If data is centralized in remote data pools, then data management becomes simplified, but latency increases
Solution Approach 1:
By segmenting data into locally distributed pools near their sources, the patent eliminates the need to transport data across long distances to centralized repositories. This segmentation maintains management simplicity through the data mesh while dramatically reducing access latency by keeping data close to where it is needed.
Solution Approach 2:
The patent implements preliminary action by pre-positioning data pools near their sources before data access is needed. This advance preparation ensures that data is already in optimal locations, eliminating transmission delays and reducing latency without requiring complex real-time data movement management.
3Loss of information
If data is distributed across multiple locations, then data loss is reduced, but data management complexity increases
Solution Approach 1:
The patent introduces a data catalog as an intermediary layer that mediates between distributed data pools and data consumers. This intermediary provides a unified view and management interface for distributed data, reducing the perceived complexity while maintaining the benefits of data distribution for loss prevention.
Solution Approach 2:
The data mesh architecture implements universality by creating a standardized framework that handles multiple data management functions (storage, access, governance, security) across all distributed data pools through common protocols and interfaces. This multi-functionality reduces complexity by providing consistent management mechanisms regardless of data location.
4Quantity of substance
If data is moved to centralized storage, then storage efficiency improves, but computing resources are wasted due to data transmission
Solution Approach 1:
By segmenting data storage into local pools near sources, the patent eliminates the need to move large volumes of data across the network for storage consolidation. This segmentation maintains storage efficiency locally while avoiding the energy-wasting data transmission that would occur with centralized storage migration.
Solution Approach 2:
The patent implements self-service by enabling data pools to autonomously manage their own storage and access operations without requiring centralized data movement. This self-service approach maintains storage efficiency at each location while eliminating the computing resources wasted on unnecessary data transmission and migration operations.
Data Source
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AI summary
A distributed data mesh system for networks is described herein. The distributed data mesh system includes data organized into separate domains, where each domain includes one or more data products representing the data in the domain. The distributed data mesh system additionally includes a data infrastructure catalog that includes information describing the data products and data domains. The distributed data mesh system is also designed based on a set of governing principles which govern how data is created, used, and maintained.