Introduction
Timeline
| Phase | Time |
|---|---|
| 1 | 30 November 2021 |
| 2 | 30 May 2022 |
Phase-1 Goals
- Running ML-Framework with at least 3 existing models for NFV.
- Generate Synthetic Data using ML.
- 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
- Build, test and optimize at least 2 models for NFV Usecases
Phase-1 Weekly Activity
These activities are
| 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 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 | Knowledge Transfer, Handoff (Buffer) | 1 |