In adoption of openRAN ecosystem the biggest impediment for operators is seamless operation between different RAN components. Deployment of openRAN does introduce some challenges as compared to traditional network architecture approach. Integration of disaggregated components needs proper alignment and automation. This raises the importance of an orchestrator in an openRAN ecosystem.
In a typical virtual network all the network functions are defined, designed and engineered by virtual network functions (VNFs). Further to intelligently automate these VNFs, orchestrator enable features like FCAPS, analytics, automated workflows, scaling, healing and zero touch provisioning (ZTP).
STL has developed an orchestrator which will provide inherent intelligence to network operations. This platform is completely aligned to open source community ONAP with standards specs defined in ETSI, ORAN etc. This AI-ML enabled orchestrator will automate workflows, create standard service and resource templates, standardise interfaces and adapters to effectively communicate and control the underlying systems. This will enable autonomous self-management operations like auto scaling, auto healing, closed loop automation and zero touch provisioning.
Standardized and disaggregated design: STL follows standardized configuration modelling language for the data modelling across services, resources and infrastructure domains. It helps indeployment of VNFs by enabling containerization, service creation and nested services scenarios.
Multi-level orchestration: It enables VNFs across multi-level domains including global data centre, regional data centre and edge cloud locations. This enables us to implement use cases for distributed analytics requirements across different levels.
Supports heterogeneous cloud regions: This orchestration platform provides support for heterogeneous cloud regions i.e. OpenStack and Kubernetes.
Strong policy driven orchestration: It supports dynamic configuration management through centralized policy management framework. It also provides support for closed control loop through ONAP’s CLAMP/Policy/SDC.
Centralized logging and monitoring: This platform gives a single view of deployed services and their relationships. Policies will be triggered for healing and scaling by analytic services, based on the data collected in VES collector.
Built in cloud native support : It supports kubernetes infrastructure management like VNF packaging in helm charts, multi tenancy, multi cluster application scheduler, service mesh controller, CA key distribution controller, multi-networking configuration support controller and cloud distribution service manager.
ML deployment : It supports the deployment of trained ML Models in dockerized containers within its analytics platform.
For some of the specific services, there is a need to create a separate network slice to meet their stringent SLAs. STL orchestrator creates various logically independent network slices to support specific services and manages it dynamically. In order to manage the lifecycle of the network functions along with provisioning services and providing closed loop control— STL has developed a model driven orchestrator which will be able to perform below functions:
Map the service related requirements to the slice related requirements
Orchestrate and manage slicing at resource, network slice instance and service layer
Manage the various lifecycles of the slice— preparation, instantiation, configuration, activation, runtime and decommissioning
Its model driven templates will be used to create independent slices within the network by dynamically placing the network functions in such a way that it can match the services requirements
Enable closed loop control along with hybrid infrastructure management to orchestrate and manage both physical as well as virtual resources used in the slice creation