NFV Orchestrator Inference Engine for VNF Parameter Automation
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Solution Overview
Problem
Deploying virtual network functions (VNFs) in network functions virtualization (NFV) is error-prone due to the complexity of providing multiple user input configuration parameters, especially during auto-scale operations, which can be cumbersome and prone to errors.
Innovation Solution
A recommendation engine is used to infer and provide recurring parameters to the orchestrator, reducing the need for users to supply these parameters manually by leveraging stored data from previous instantiations, thus simplifying the deployment process across multiple instances and virtual infrastructure managers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration parameters are provided for each VNF instantiation, then deployment control and flexibility are maintained, but deployment complexity and error rate increase significantly
Solution Approach 1:
The system performs preliminary action by storing configuration parameters from previous VNF instantiations in a database before new deployment requests occur. When a new VNF needs to be deployed, the system retrieves and reuses these pre-stored parameters, eliminating the need for manual reconfiguration and reducing both complexity and errors.
Solution Approach 2:
The system creates and stores copies of configuration parameters from previously deployed VNFs. These parameter copies are then retrieved and applied to new VNF instantiations, allowing rapid deployment without manual re-entry of identical configuration data, thereby reducing complexity while maintaining deployment accuracy.
2Ease of operation
If all configuration parameters are required for each VNF deployment, then complete control over deployment is maintained, but user effort and time consumption increase
Solution Approach 1:
The system extracts and separates commonly recurring configuration parameters from the manual input process by automatically retrieving them from stored historical data. This extraction eliminates the need for users to manually provide these parameters, significantly reducing user effort and deployment time while maintaining control over the deployment process.
Solution Approach 2:
The system performs self-service by automatically retrieving, selecting, and applying appropriate configuration parameters from its stored database without requiring manual user input. This automation reduces both the effort users must expend and the time required for deployment, while the system maintains full control over the parameter selection process.
3Productivity
If configuration parameters are stored and reused from previous instantiations, then deployment speed and efficiency improve, but parameter accuracy and applicability may decrease
Solution Approach 1:
The system applies dynamics by making the parameter selection process adaptive rather than static. When retrieving parameters from stored data, the system dynamically adjusts based on the specific requirements of the current VNF deployment context, selecting only those parameters that are truly applicable and relevant, thereby maintaining both efficiency and accuracy.
Solution Approach 2:
The system applies local quality by selectively retrieving and applying only those configuration parameters that are specifically relevant to the current VNF deployment scenario, rather than blindly reusing all stored parameters. This selective approach ensures that each deployment receives the precise parameters it needs, maintaining accuracy while benefiting from automated retrieval efficiency.
Data Source
AI summary
A system and method for deploying a virtual network function (VNF) are disclosed. Deploying a VNF includes receiving a request to instantiate a VNF in a network virtualization infrastructure, obtaining input from a user providing parameters needed for performing the instantiation of the VNF, determining a type of deployment for the VNF, and adding parameters inferred from the type of deployment to the user data to complete the parameters needed for deployment of the VNF, wherein the added parameters are inferred based on stored data regarding previous instantiations of the VNF. Determining the type of deployment for the VNF includes determining a number of instances of the VNFs to be deployed and a number of virtual infrastructure managers that will be instructed to deploy resources needed by the VNF.