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 particular category. For example, the first category we have chosen is Failure prediction, and we aim to create 6 models - failure prediction of VMs. Containers, Nodes, Network-Links, Applications, and middleware services. This project also aims to define set of data models for each of the decision making problems, that will help both provider and consumer of the data to collaborate.
Name
Thoth
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)
Intern
Key-Info (info.yaml)
---
project: 'Thoth'
project_creation_date: '01 June 2021'
project_category: 'Infrastructure' # One of: Deployment, Integration & Testing, Infrastructure
lifecycle_state: 'Incubation' # One of: Incubation, Mature, Integration, Archived
project_lead: &anuket_PROJECTNAME_ptl
name: 'Sridhar K. N. Rao'
email: 'sridhar.rao@spirent.com'
company: 'Spirent Communications'
id: 'sridharkn' # Linux Foundation ID
timezone: 'IST ((GMT+5:30)'
primary_contact: *anuket_PROJECTNAME_ptl
issue_tracking:
type: 'jira'
url: 'https://jira.anuket.io/projects/thoth'
key: 'thoth'
mailing_list:
type: 'groups.io'
url: 'anuket-tech-discuss@lists.anuket.io'
tag: 'thoth'
realtime_discussion: # Fields may be blank if no realtime discussions
type: 'slack'
server: 'anuketworkspace.slack.com'
channel: '#thoth'
meetings: # Fields may be blank if no standing meetings
- type: 'zoom'
agenda: 'https://wiki.anuket.io/display/HOME/Thoth-Meeting+Minutes' # eg: 'https://wiki.anuket.io/display/HOME'
url: 'https://zoom.us/j/96163911066' # eg: 'https://global.gotomeeting.com/join/819733085'
repeats: 'weekly' # ex: weekly, monthly, bi-weekly
time: '13:00 UTC' # ex: '16:00 UTC'
repositories:
- 'thoth' # ex: myproject
committers:
- <<: *anuket_CHANGEME_ptl
- name: 'Sridhar K. N. Rao' # repeat all fields for each committer
email: 'sridhar.rao@spirent.com'
company: 'Spirent Communications'
id: 'sridharkn'
- name: '' # repeat all fields for each committer
email: 'girish.l@cittumkur.org'
company: 'CIT, Gubbi'
id: 'girishl'
- name: '' # repeat all fields for each committer
email: 'rohitrathore.imh55@gmail.com'
company: 'BIT, Mesra'
id: 'TeAmP0is0N'
tsc:
# yamllint disable rule:line-length
approval: '' # ex: https://wiki.anuket.io/display/HOME/2021-01-12+TSC+Agenda+and+Minutes
changes:
- type: ''
link: ''
# yamllint enable rule:line-length
Committers
Volunteers
In discussion: 4 CS/Math Students.
Meeting Details
Topic: AI/ML for NFV
Time: 13:00 Universal Time UTC
Day: Every week on Friday
Zoom Link: https://zoom.us/j/96163911066
Meeting ID: 961 6391 1066
Find your local number: https://zoom.us/u/acEvZCMvjT
Weekly Meeting minutes
Volunteer Contributions
| Sl. No. | Contributor | Contribution | Duration | Certificate of Appreciation OR Contribution |
|---|---|---|---|---|
| 1 | Girish L | Survey of:
| 1 Month | |
Timeline and Goals
| Phase | Time |
|---|---|
| 1 | 30 November 2021 |
Phase-1 Goals
- Running ML-Framework with at least 3 existing (enhanced) 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-1 Bonus
- Build Two Tools
- AlgoSelector
- TVLVapp
Phase-1 Weekly Activity
12 weeks, if the Intern is working Full-time.
| Sl. No. | Activity by Intern/Researcher(s) | Week | Comment / Support from Advisor (s) | Updates by Rohit Singh Rathaur |
|---|---|---|---|---|
| 1 | Understand the state of art - Publications and OS projects Analyze the Gaps. Create a 1-Page report based on the analysis. Identify the problems in NFV for which the techniques are still not good enough. | 1.5 | Share the State of the art survey. Provide initial gap analysis. | Understand the art of publications and OS projects. Decided to go with LFN Acumos. Chose a problem domain: Failure Prediction to start working with. Completed the reading papers related to Failure Prediction and updated the implementation details till now whatever I have got. Status: Completed 1-page report where mentioned failures and what type of failures. |
| 2 | Deploy the ML Framework (Tentative: LFN Acumos).
| 1.5 | Provide access to the server(s). Intel Pod? | Reading about RNNs to work with existing FP models. Agreed to work with Tensorflow and LF Acumos. Got the Intel Pod 12 access and successfully connected. Now working on deploying Acumos. Completed the survey part and working on the installation of Acumos but still, it's failing. I was not able to run docker, so still figuring it out. But In the meantime, I am working on reproducing the failure prediction work using the local environment. Updated the sheet with implementation details but mostly codes are not open-source yet. |
| 3 | Collect, analyze and document the 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 | Suggestions 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 |