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https://github.com/5uperpalo/success6g-edge

https://github.com/5uperpalo/success6g-edge

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: 5uperpalo
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 10.7 MB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

Documentation: https://5uperpalo.github.io/success6g-edge/

| Architecture_Overview_Basic | | :---------------------------------------------------------------------------------------------------------------------------: | | 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 * data and notebooks directories include analysis code used for initial edge model deployment and testing.

Solution overview

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

Additional ideas

  • 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

Owner

  • Name: Pavol Mulinka
  • Login: 5uperpalo
  • Kind: user
  • Location: Barcelona, ES
  • Company: CTTC

Data Scientist / Machine learning Enthusiast & former network engineer

GitHub Events

Total
  • Issue comment event: 1
  • Push event: 9
  • Pull request event: 5
  • Create event: 1
Last Year
  • Issue comment event: 1
  • Push event: 9
  • Pull request event: 5
  • Create event: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 0
  • Total pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 11 months
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • 5uperpalo (3)
  • mf-idneo (2)
Top Labels
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Dependencies

requirements.txt pypi
  • lightgbm *
  • prometheus-client *
  • redis *