Sandbox Orchestration for Cognitive Network Functions
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Solution Overview
Problem
Current systems lack a mechanism for orchestrating sandboxing of cognitive network management functions, particularly in Cognitive Autonomous Networks (CAN), which is essential for building trust and training these functions before deployment, especially given the unpredictability of machine learning algorithms and the need for non-intrusive training environments.
Innovation Solution
A Sandbox Orchestration Function (SOF) is introduced to manage and allocate sandbox resources, including simulation environments, emulators, and digital twins, to test and train cognitive functions, determining the appropriate sandbox based on the function's trust level and deployment context, and controlling the execution of these functions between sandbox and real networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cognitive functions are trained offline or in sandbox environments, then the risk of unwanted decisions in real networks is minimized, but the training time and deployment complexity increase
Solution Approach 1:
The system performs preliminary training of cognitive functions in sandbox environments before deploying them to real networks. The sandbox orchestrator prepares training data, configures sandbox environments, and executes training workflows in advance, so that functions are ready for deployment without requiring extensive on-site training time.
Solution Approach 2:
The sandbox orchestrator acts as an intermediary between the training environment and the real network. It manages the transition of cognitive functions from sandbox to production, handling resource allocation, environment configuration, and deployment coordination to minimize overall training and deployment time.
2Measurement precision
If sandbox environments are configured with reserved resources for specific use cases, then the training accuracy and contextual relevance improve, but the system complexity and resource management overhead increase
Solution Approach 1:
The sandbox orchestrator implements self-service mechanisms for resource management. It automatically provisions sandbox environments, allocates computing resources, and configures training parameters based on the specific cognitive function being trained, reducing the need for manual configuration and lowering operational complexity.
Solution Approach 2:
The sandbox orchestrator is designed as a universal platform that can handle multiple types of cognitive functions, training algorithms, and resource configurations through a single interface. It supports various sandbox environments (emulators, simulators, test networks) and can dynamically adapt to different training requirements without requiring separate management systems.
3Adaptability or versatility
If cognitive functions from different vendors are integrated, then the system versatility and functionality improve, but the unpredictability and integration complexity increase
Solution Approach 1:
The sandbox orchestrator enforces homogeneous interfaces and protocols for all cognitive functions regardless of vendor origin. It standardizes the sandbox environment configuration, resource allocation mechanisms, and deployment procedures, allowing functions from different vendors to be integrated without increasing complexity.
4Reliability
If trust levels are established for cognitive functions, then the operational safety and reliability improve, but the deployment process and monitoring overhead increase
Solution Approach 1:
The sandbox orchestrator implements automated feedback mechanisms that monitor cognitive function performance in sandbox environments and automatically update trust levels based on observed behavior. This automated trust assessment reduces manual monitoring overhead while maintaining high operational safety standards.
Data Source
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AI summary
The present invention provides apparatuses, methods, systems, computer programs, computer program products and computer-readable media regarding orchestrating sandboxing of cognitive network management functions. The method for use in a management entity of a communication network, the management entity having an interface to at least one cognitive function module comprises obtaining, at the management entity, information about a trust level of an action of at least one cognitive function module, determining whether the trust level of the at least one cognitive function module fulfills a predetermined trust level, and if it is determined that the first predetermined trust level is fulfilled, allowing the action of the at least one cognitive function module to be executed on the communication network under specific conditions.