Central Metadata Repository for Scalable SDN Data Discovery
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Solution Overview
Problem
The integration of data services in software-defined networks (SDNs) is hindered by the complexity of data discovery and the lack of a centralized data inventory, leading to inefficiencies in resource allocation and system scalability.
Innovation Solution
A scalable integrated information system comprising a central metadata repository and a reasoning module that communicates with a network environment to retrieve and store metadata inventory, utilizing a machine learning module to develop a reasoning model for actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized metadata repository is implemented to improve data discovery efficiency, then data service scalability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a centralized metadata repository as an intermediary component that mediates between data sources and data services. This repository stores structured metadata about available data assets, enabling efficient data discovery without requiring direct integration between all system components. The metadata repository acts as a mediator that simplifies the overall system architecture while improving productivity.
Solution Approach 2:
The system segments data management functionality by separating metadata storage from actual data storage. The metadata repository contains structured information about data assets (locations, formats, relationships) while the actual data remains in distributed data sources. This segmentation allows independent optimization of each component and reduces system complexity.
2Loss of information
If automated reasoning is applied to generate actionable insights, then information quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring metadata in the centralized repository before actual data analysis is needed. Metadata is organized with standardized schemas, relationships, and contextual information in advance, so that when automated reasoning is applied, the processing can proceed more efficiently with pre-validated and structured information available.
3Adaptability or versatility
If virtualized network functions are deployed to improve flexibility, then adaptability is improved, but resource management complexity increases
Solution Approach 1:
The centralized metadata repository serves multiple functions: it stores metadata, provides data discovery capabilities, supports data governance, and enables automated reasoning. This multi-functional approach consolidates several complex management tasks into a single universal component, improving adaptability while managing complexity through functional integration rather than proliferation of separate systems.
Data Source
AI summary
A scalable integrated information system in a network environment, the system comprising: an agent instantiated as a virtual machine or virtual network function, the agent configured to communicate with the network environment, the network environment comprising a meta data inventory; a data store comprising a central metadata repository, the central metadata repository configured to communicate with the network environment and selectively retrieve the meta data inventory, wherein the central metadata repository stores an integrated context representation comprising at least one of a real-time temporal context, a historical context, and a meta context associated with the meta data inventory; a reasoning module instantiated as a virtual machine or virtual network function and including an input configured to receive a reasoning concept; a machine learning module, instantiated as a virtual machine or virtual network function and configured to communicate with the central metadata repository to selectively retrieve the integrated context representation, wherein the machine learning module communicates with the reasoning module to develop a reasoning model configured to associate the reasoning concept with the integrated context representation; and wherein the agent communicates with the data store to retrieve the integrated context representation and communicates with the reasoning module to retrieve the reasoning model to develop an action and wherein the agent implements the action within the environment.