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- Running ML-Framework with at least 3 existing models for NFV.
- Generate Synthetic Data using ML.
- Create Identify 3 problems for which ML can be applied in NFV - For which no acceptable models exist.
- Identify the ML technique that can be used for these problems.
Phase-2 Goals
- Add at least 1 new model to the ML Framework. Build, test and optimize at least 2 models for NFV Usecases
Phase-1 Weekly Activity
These activities are
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| Sl. No. | Activity by Intern/Researcher(s) | Week | Comment / Support from Advisor (s) | |||
|---|---|---|---|---|---|---|
| 1 | Understand the state of art - Publications and OS projects Analyze the Gaps. Create 1-Page report based on the analysis. Identify for the problems in NFV for which the techniques are still not good enough. | 1.5 | Share the State of art survey. Provide initial gap-analysis. | |||
| 2 | Deploy the ML Framework (Tentative: Acumos).
| 1.5 | Provide access to server(s). Intel Pod? | |||
| 3 | Collect, analyze and document implementation of 3 existing models for NFV. Collect the | test-data. | 1 | Provide the 3 models to use. | ||
| 4 | Deploy the models on the framework (2) Collect the data (contd). | 1 | None. | |||
| 5 | Test and optimize the models - If possible. | 2 | Suggestion for optimization approaches. | |||
| 6 | Study ML technique for Synthetic time-series data generation (STSDG) | 1 | Suggest the right technique | |||
| 7 | Implement the technique for STSDG | 2 | ||||
| 8 | Test and optimize STSDG | 1 | ||||
| 9 | 10 | Knowledge Transfer, Handoff (Buffer) | 1 | 11 | 12