Autonomous Network Controller Modules for Adaptive Self-Configuration
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Solution Overview
Problem
Complex communication networks face challenges in adapting to emerging issues and integrating new technologies with minimal human intervention, as they become increasingly complex and dynamic.
Innovation Solution
The implementation of an autonomous network architecture using composable and replaceable modules, where controllers are configured in a system hierarchy graph, allowing for online experimentation and evolution, enabling the network to adapt and evolve with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the network becomes more complex to handle emerging issues and integrate new technologies, then the network's adaptability and functionality improve, but the difficulty of understanding and management increases
Solution Approach 1:
The network is divided into autonomous network slices, each independently managed and optimized for specific functions or services. This segmentation allows the network to handle diverse requirements without increasing overall management complexity, as each slice can be configured and controlled independently.
Solution Approach 2:
The network employs dynamic resource allocation and configuration capabilities, allowing network parameters, resources, and topology to be adjusted in real-time based on changing requirements. This dynamic approach enables the network to adapt to emerging issues without permanent structural changes, maintaining manageability while improving versatility.
2Extent of automation
If human intervention is reduced to achieve autonomous operation, then operational efficiency improves, but the ability to handle unforeseen events may deteriorate
Solution Approach 1:
The autonomous network incorporates continuous monitoring and feedback mechanisms that detect unusual conditions and trigger appropriate responses. The system learns from observed patterns and can identify unforeseen events through anomaly detection, maintaining reliability while operating autonomously.
Solution Approach 2:
The network implements self-diagnosis, self-configuration, and self-healing capabilities that allow it to handle unforeseen events independently. When anomalies are detected, the system automatically adjusts parameters, reconfigures resources, or isolates problematic components without human intervention, maintaining both automation and reliability.
Data Source
AI summary
A controller for an autonomous network includes a processor configured to execute composable and replaceable modules in an interconnected manner to configure controller components. The controller components comprise a sensing component configured to collect sensor data about at least one controlled element under control of the controller, an analyzing component configured to process the collected sensor data to derive a current state of the at least one controlled element, a deciding component configured to, based on the derived current state, decide an action to be made with respect to the at least one controlled element, and an acting component configured to carry out the decided action with respect to the at least one controlled element, by causing a change in at least one of an operation of the at least one controlled element, or a configuration of the at least one controlled element.