Introduction

AI has potential in creating value in terms of enhanced workload availability and improved performance and efficiency for NFV usecases. This work aims to build Machine-Learning models and Tools that can be used by Telcos (typically by the operations team in Telcos). Each of these models aims to solve single problem within a single particular category. For example, the first category we have chosen is Failure prediction, and we aim to create 4 6 models - failure prediction of VMs, . Containers/Pods, Nodes, and Applications  Network-Links, Applications, and middleware services.

Approach

Decision Driven Data Analytics. 

https://mitsloan.mit.edu/ideas-made-to-matter/decisions-not-data-should-drive-analytics-programs

PTL

Sridhar K. N. Rao (Sridhar Rao)

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1. Running ML-Framework with at least 3 existing (enhanced) models for NFV.
2. Generate Synthetic Data using ML.
3. Identify 3 problems for which ML can be applied in NFV - For which no acceptable models exist.
   1. Identify the ML technique that can be used for these problems.

Phase-1 Bonus

1. Build Two Tools
   1. AlgoSelector
   2. TVLVapp 

Phase-1 Weekly Activity

12 weeks, if the Intern is working Full-time.

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