4 OCNWDAF Features

This chapter describes OCNWDAF features.

4.1 Databases

The OCNWDAF uses the following databases:

  • The cnDBTier is used for cross-site transactional, dynamic, or configuration data. The cnDBtier is one of the Oracle's 5G Common Services and it used by all Oracle 5G NFs.
  • MySQL Enterprise is used as the Analytics Database. It stores all historical data as well as the ML models. The same data is available across all OCNWDAF sites, this ensures all sites support all queries.

4.2 Support for ML Algorithms

The OCNWDAF supports the industry's best ML frameworks, such as TensorFlow and scikit-learn. Many supported models, including Neural Networks and specialized versions such as Long Short Term Memory, are used.

Oracle’s AutoMLx is also used to determine the optimal algorithm for each analytics category. The algorithm is selected based on specific requirements such as the time required to train the ML model and the accuracy.

The OCNWDAF provides a GUI dashboard to select, train, and optimize one or more ML Models for a given analytics category. The user can select among multiple algorithms supported by each analytics category and run experiments to determine the best-suited ML model for each data set. ML models are evaluated by running experiments and metrics are generated. Metrics for each experiment is displayed on the dashboard. User can select the ML model based on these metrics. Multiple algorithms can be selected simultaneously to run experiments. For more information, see Oracle Communications Networks Data Analytics Function User Guide.

4.3 Support for ML Model and Repository

The main constraints of machine learning models are:
  • The time necessary to train the model
  • The accuracy observed from the model

We are focusing on a two-prong approach described below to ensure both the constraints are minimized:

  • The ML models will be developed for the use cases and analytics categories will be used in a very general and broad manner. User can select the best suited algorithms for each analytics category and train the ML models.
  • The ML models can automatically self-optimize by using the industry’s best hyper-parameter optimization algorithms. This ensures enhanced accuracy.

All models trained are stored in the Open Neural Network eXchange (ONNX) format. This format is used widely in the industry. Using this format enables future support for importing and exporting models.

4.4 OCNWDAF Multicloud Strategy

Oracle is a platinum member of the Cloud Native Computing Foundation (CNCF) and aligns with the CNCFs vision of an open, cloud-native, and standard approach to develop applications. Based in-depth understanding of service reliability, Oracle strives to create foundational platform services where applications are built to provide service reliability.

Oracle's OCNWDAF and Cloud Native Environment (CNE) are based on industries leading open-source services and tools, this enable operations and management of OCNWDAF in a production-grade environment. Oracle has adopted software architecture principles to support Communications Service Providers multicloud strategy.

  • The Cloud Native Framework represents a collection of shared services which OCNWDAF uses. Oracle maintains a reference Cloud Native Framework utilizing a selection of open-source components and tools for development. The deployment CNE must provide all the Cloud Native Framework services but is free to select the specific version and component providing the service. For example, Oracle's OCNWDAF supports operation with CSPs' version of Prometheus and can support either Jaeger or Zipkin for distributed tracing. A DevOps CI/CD model is supported to ensure OCNWDAF is thoroughly tested and is fully operational in the deployment environment.
  • Kubernetes' environment provides runtime and lifecycle management services. Like the Cloud Native Framework, Oracle maintains a reference Kubernetes environment but allows CSPs to select the specific version used in the deployment CNE. OCNWDAF runs in a containerized environment, uses Kubernetes DNS for service discovery, and can support a variety of Container Network Interfaces which integrate Oracle NFs networking requirements with the deployment SDN environment. The services lifecycle is controlled, deployment specific configuration is permitted and environment files are dynamically associated with the OCNWDAF. Helm charts are used for packaging the component microservices into the OCNWDAF applications. The configured repository handles the versioning available for each service and component microservices. Operators can enable Kubernetes deployments to manage the lifecycle of the OCNWDAF, including the initial roll-out of the service, roll-out of a canary release to validate a new version, and roll-out of an upgrade to a new version.
  • Oracle continuously delivers new functionality into the Oracle Portal. Customers are notified and may choose to trigger their CI/CD workflow to deploy the new functionality. Oracles Communications Global Business Unit implements and executes automated unit tests for each component and service as part of the build process. Automated integration tests are run using the reference Cloud Native Framework to ensure that each component's runtime services and exposed APIs operate as expected. Other automated integration tests ensure APIs and NF components are functional and the overall business logic of the NF is operational. Oracle CGBU includes the subset of these test artefacts (which validate operation with the runtime environment and Cloud Native Framework) with the images of the NF components. Customers can incorporate these automated tests in the target deployment environment.
  • Oracle Communications Automated Test Tools and Scripts (ATS) helps operators to automate the complete testing lifecycle of 5G NFs. This aids in accelerating innovation as software delivery times are significantly shortened. There is a considerable benefit in deploying an automated testing solution in terms of cost, effort, and overall test coverage. With the adoption of DevOps and CI/CD in the Telecommunication domain, number of software releases has significantly increased thus making manual testing very challenging. Oracle Communications ATS helps operators to execute functional, regression and performance test cases easily without user intervention. As a result, operators can quickly deploy new software releases and rapidly roll out new features.