Attendees
Karthik
Vishnu Ram
Sl. No. | Topic | Presenter | Notes |
---|---|---|---|
1 | Thoth - ITU (a) Problems and Datasets in ITU. (b) Host and Competitor | Challenge: https://aiforgood.itu.int/about/aiml-in-5g-challenge/ Wiki Page to capture the collaboration: Collaboration - ITU
Github link for the Problems/Solutions: https://github.com/ITU-AI-ML-in-5G-Challenge Data from Real-World: Turkcell and China Unicom. | |
2 | Kubernetes - AI/ML (a) Problems (b) Data Sources. | Refer below the Problems. Logs: System components, Pods and Deployment. System Components: Fluentd. Refer to presentation by Zebrium. | |
3 | Kubeflow - An Introduction | Alternative: LF Acumos. | |
4 | Developers Workshop sessions | Please remember to propose sessions for the upcoming Developer's Conference. LFN Developer & Testing Forum, Jan 10-13, 2022
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More Links:
- This contains real network data from Turkcell - https://github.com/Turkcell/ITU-AIMLin5GChallenge-2021/blob/main/RLF_Prediction_ITU_AIML_Challenge_Data/TurkcellExampleProject.ipynb
- This contains dataset from NIST: https://github.com/usnistgov/PS-002-WALDO#authors check also: https://github.com/usnistgov/PS-002-WALDO/
- GNN challenge dataset from Barcelona: https://bnn.upc.edu/challenge/gnnet2021
- Xilinx: https://challenge.aiforgood.itu.int/match/matchitem/34 uses DeepSig RadioML 2018 dataset.
- ZTE: https://wiki.lfaidata.foundation/display/ADLIK/2021+Combinatorial+Optimization+Challenge%3A+Delivery+route+optimization has the dataset
- UPF : https://www.upf.edu/web/wnrg/2021-edition uses https://zenodo.org/record/4059189
- Brazil: https://github.com/knowledgedefinednetworking/RouteNet-challenge/tree/master/data/sample_data/validation
Categories of Problems | Example Reference Works |
Performance Metric Prediction | 1.End-to-End Latency Prediction of Microservices Workflow on Kubernetes 2.Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions |
Resource Scheduling | 1.Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster 2.A Proposal of Kubernetes Scheduler Using Machine-Learning on CPU/GPU Cluster 3.Ananke: A framework for Cloud-Native Applications smart orchestration |
ML Platform on K8S* | 1.Multi-Tenant Machine Learning Platform Based on Kubernetes 2.EdgeAI: Edge-native distributed platform for artificial intelligence |
Resource Consumption Monitoring (Detection/Prediction) | 1.A Machine Learning Model for Detection of Docker-based APP Overbooking on Kubernetes 2.Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment (Energy) 3.AI-Based Resource Management in Beyond 5G Cloud Native Environment 4.Intelligent and Autonomous Management in Cloud-Native Future Networks |
Testing | Artificial Intelligence assisted Canary Testing of Cloud Native RAN in a mobile telecom system |
TE and Others | “End-to-End Management of All Optical Disaggregated Network and Applications with Cloud Native Environment” |