Item | Who | Notes |
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Rel 6 Integration Testing | Reshma | Many bugs and issues have been fixed. Thanks to Reshma, Sandeep and others. SDNC and DMaaP issues have been resolved. Link to Integration testing page: xx Outstanding issue:
- Netconf mount on Honeycomb is working. Getting 404 error for netconf config message.
- Have to test for Deny message for second CL from Policy.
- Lack of extra VM for RAN Sim. Can have limited number of RAN Sim nodes.
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Rel 7 planning |
| We have requested to present to ArchComm on 5/26. Dependency on CPS - Strong alignmentMajor points related to architecture
- Dependency on C&PS - C&PS team has consensus that initial implementation in R7 will meet needs of SON use case.
- Dependency on ORAN - In order to prevent major code changes, we need pre-standard version of O-1, VES formats, yang models
- Policy - Include Defer from CLC (high priority), Separate Drools instance (depends on Policy)
- Modeling - Cell lifecycle - Assume new
| SON use cases | - cell addition etc is not part of ONAP. We can assume that data on list of cells is available to C&PS.
- Incorporating ML-based use case. See last item.
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R7 Wiki page |
| Guilin (R7) - Use Cases . Need to to add OOF-SON in this page. Use the "Template" Use Case Tracking Template |
Incorporating ML-based SON use cases |
| Thanks to Vijay and Shankar PN who are PTLs or DCAE and OOF who joined the call. We discussed high-level guidance about incorporating ML-based SON use cases. Consensus was that training of models should be done outside ONAP, and likely to be offline. ONAP use cases should focus on demonstrating how an ML-trained model can be onboarded. This can be done as a recommendation model in OOF, and also as a DCAE MS which leverages work done in Frankfurt for the Acumos-DCAE Adapter. See link: Acumos DCAE Integration Guidance for new use case discussions: Generation of data and training of ML-model must be done outside ONAP separately. ONAP SON solution should include: pre-trained model, source (e.g. enhancement to RANSim) of data needed to apply the pre-trained model |