Documentation: https://5uperpalo.github.io/success6g-edge/
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| Basic Architecture Overview, more detailed information in architecture_overview.md |
This repository is for the edge pod implementation, monitoring, and analysis in SUCCESS6G project.
Table of contents
- detailed description of the use cases
- description of the data management and database choices
- description of the ml model choices
- explanation of the communication between the services
- description of Research&Development setup
- description of Testing setup
- inference model helm chart
- a guide to implementing needed services
- computational requiremetns of the services
- vehicle injector tool - tool to inject example v2x data into Redis database
- example v2x sensor data provided by Idneo
- example v2x aggregated sensor data provided by Idneo
dataandnotebooksdirectories include analysis code used for initial edge model deployment and testing.
Solution is deployed in Microk8s.
Description of the components:
- Grafana - dashboards
- Ingress - expose services to the operator
- Prometheus - gather pod metrics
- InfluxDB - gather vehicular measurements and predictions
- MinIO - store models and training/testing data
- JupyterHub - develop new models
- MLflow - MLops, experiment and model tracking
- Kserve - serve inference models to predefined pods
- Istio - to ensure optimal traffic flow between microservices
- Knative - to ensure autoscaling of inference service pods
- Kepler - gather energy consumption data
- Redis - API for transfer of OBU measurements to Kubernetes
- KubeEdge deployment - same as Microk8s except with KubeEdge and Kubeflow/Kserver is swapped for Sedna
- implement multimodel pods e.g. by ModelMesh, or alpha feature of Kserve
- use Rancher to manage multi cluster Kubernetes
- implement Kserve inference service as gRPC for high-performance/low latency production implementation