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Team
Sno | Name | Organization | Module | |
|---|---|---|---|---|
| 1 | Jaegon Kim | Samsung Electronics Co., Ltd, Korea | SAFe Framework Design | jaegon77.kim@samsung.com |
| 2 | Karthikeyan Subramaniam | Samsung R&D Institute of India | SAFe Framework Design, Netflow | karthikeyan.s@samsung.com |
| 3 | Saritha Ramesh Thangaraju | Samsung R&D Institute of India | Analytics Manager, Usecase Workflows | |
| 4 | Senthil Subramaniam | Samsung R&D Institute of India | Sflow, Data Monitor, Usecase Workflows |
s. |
| 5 | Sudhakar B | Samsung R&D Institute of India | AL/ML Adapter, Usecase Workflows | |
| 6 | Tae woo Kim | Samsung Electronics Co., Ltd, Korea | SAFe Framework Design | t01.kim@samsung.com |
Abstract
Irrespective of underlying network technologies, the network can be intelligently controlled with Software Defined Networking(SDN). The SDN control software can manage various network elements including switches, routers, and virtual switches agnostic to vendors. The network administrators use SDN for rapid deployment including configuration, monitoring, and troubleshooting devices across SDN-controlled networks. SDN is being adapted at a faster pace to accommodate the evergrowing Network traffic needs. The Network administrator spends a considerable amount of time in repetitive activities like Network Software upgrades, Monitoring, Troubleshooting, etc. Typically, the SDN controller does not analyze traffic conditions which are required for the network administrators to optimize resource utilization, service quality, anomaly detection and etc. To address this issue, we are proposing to introduce Smart Automation Framework (SAFe) in ONOS. We also investigated a few issues like Network devices software upgrades and Resource Utilization. Our investigation proved that the SAFe improves operational efficiency (i.e., minimal or zero traffic loss, less operational cost) when compared to the manual procedure. The SAFe can handle workflow-based applications like optimal resource monitoring, anomaly detection, AI/ML-driven configuration, etc.
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Sequence Diagram – 3. Initiate Data Training
Sequence Diagram – 4. Prediction
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