Edge Configuration Data Distribution Using JSON Scoping
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Solution Overview
Problem
In complex computing networks, particularly in edge computing environments, managing and distributing configuration data across thousands of edge servers is cumbersome due to the volume and complexity of configuration information, which traditional centralized relational databases struggle to handle efficiently.
Innovation Solution
A system utilizing non-relational, text-based data formats like JSON to store and distribute configuration data, enabling flexible management and distribution of configuration settings based on various scoping parameters, and employing a configuration system with tools for defining, filtering, and distributing configuration tags and values across edge nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional centralized relational databases and table structures are used to store configuration data, then data storage and retrieval mechanisms are established, but the system becomes unwieldy and difficult to manage in edge computing environments with thousands of edge servers
Solution Approach 1:
The patent segments configuration data into hierarchical structures with configuration documents, sections, and key-value pairs. This segmentation allows the system to manage configuration data in smaller, more manageable units that can be independently processed and distributed to edge servers, reducing overall system complexity while maintaining ease of operation.
Solution Approach 2:
The patent uses JSON format to create a copy of configuration data that can be easily replicated and distributed across multiple edge servers. This copying approach enables configuration data to be shared without requiring complex relational database transactions, simplifying both storage and distribution operations.
2Quantity of substance
If configuration data is stored in voluminous and complex formats, then comprehensive configuration information can be stored, but distribution and maintenance become difficult
Solution Approach 1:
The patent changes the data format parameter from traditional relational database tables to JSON format. This parameter change allows the system to maintain comprehensive configuration information while improving distribution efficiency, as JSON data can be easily parsed, filtered, and transmitted across network boundaries without complex query processing.
Solution Approach 2:
The patent organizes configuration data in a hierarchical dimension with documents, sections, and key-value pairs. This dimensional organization allows voluminous configuration data to be structured in a way that facilitates efficient distribution and maintenance, as data can be selectively transmitted based on hierarchy levels rather than requiring full data transfers.
3Reliability
If all configuration data is distributed to all edge servers, then comprehensive coverage is achieved, but unnecessary data transport across the network increases latency
Solution Approach 1:
The patent extracts only the necessary configuration data for each edge server based on its specific role and requirements. By taking out only relevant configuration sections and key-value pairs rather than distributing all configuration data, the system maintains comprehensive coverage while reducing unnecessary network traffic and latency.
Solution Approach 2:
The patent applies local quality by customizing configuration data distribution to match the specific needs of each edge server. Different edge servers receive different configuration data based on their local requirements, ensuring that each server receives only the configuration information it needs for its specific function, thereby reducing network latency while maintaining reliability.
Data Source
AI summary
Systems and methods for ingesting, managing, and distributing configuration data in one or more computing networks are provided. In examples, a flexible configuration definition framework is provided to allow for simplified ingestion, management, and distribution of configuration data to various computing devices in complex networks. Rather than table data, the framework permits expression of configuration settings in a non-relational, text-based data format to allow easy searching and filtering of configuration data and targeted distribution of data to machines and applications within the network(s).