Autonomous Network Module Graph for Coupled Function Orchestration
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Solution Overview
Problem
Existing autonomous network (AN) technologies face challenges in efficiently training and inferring strongly coupled functions, leading to high computational and memory costs due to interdependent functions sharing common input metrics and output control parameters, which are typically trained and inferred in isolation.
Innovation Solution
A modular, programmable, and standards-compliant network automation system that organizes AN functions as interchangeable modules, enabling efficient building, orchestration, and adaptation of interdependent functions through a module-based graph and metadata retrieval, allowing seamless coordination and reuse of modules across different network environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If interdependent AN functions are trained and inferred in isolation, then each function can be developed independently, but computational expenses and memory costs increase significantly
Solution Approach 1:
The patent merges strongly coupled AN functions into a unified modular framework where functions share common input metrics, features, and output control parameters. This consolidation enables joint training and inference across multiple functions, eliminating redundant computational operations and significantly reducing overall computational expenses and memory requirements while maintaining independent developability through modular architecture.
2Adaptability or versatility
If interdependent AN functions are trained and inferred in isolation, then development flexibility is maintained, but memory and storage costs increase substantially
Solution Approach 1:
The patent implements a universal modular framework where a single set of shared components (input metrics, features, control parameters) serves multiple AN functions simultaneously. This multi-functional architecture allows different functions to be developed and deployed independently while sharing common resources, thereby maintaining development flexibility while substantially reducing memory and storage requirements through resource reuse.
3Productivity
If modular AN functions are coordinated through metadata retrieval, then adaptation efficiency improves, but system complexity increases
Solution Approach 1:
The patent introduces metadata as an intermediary layer that mediates between the modular AN functions and the coordination system. This metadata framework enables efficient adaptation by providing structured information about function dependencies, input metrics, and output parameters, allowing the system to automatically coordinate functions without requiring complex manual configuration. The metadata acts as a simplified interface that manages system complexity while improving productivity.
Data Source
AI summary
Disclosed are devices, methods, apparatuses, and computer-readable media for autonomous networks. An example apparatus for an autonomous network service provider may include at least one processor and at least one memory. The at least one memory may store instructions that, when executed by the at least one processor, may cause the apparatus at least to: receive a first request for a first application; retrieve by metadata retrieval function, first metadata for the first application; and retrieve by module retrieval function, one or more first modules of input, one or more first modules of function and at least one first module of task according to the retrieved first metadata.