https://wiki.lfnetworking.org/pages/viewpage.action?pageId=56067017

1. Key Takeaways: Emphasis is too much on Data - Data First. I have these data, what can I do, approach.  We should come up the "Decisions" that Telcos are interested.
2. Problem Statement section include assumption
   1. "it is not a competitive advantage to have access to better AI tools" - Tools are not important, its the models, and Models definitely can give competitive advantage.
3. Need to be careful about how to share data and results across competing companies - Sharing with Opensource projects may be easier than "competing companies"
   1. "Opensource Licensing Model" for Data - Ex: Whatever model built using(learning) the data, that should be OPEN tool. 

Where Thoth can contribute to EUAG:

1. Data Model - For each of the problem statement ("Decision"), we can propose a data model.

https://drive.google.com/file/d/1FdhT4d8QHQR7OqfXhx3UqviGDYW5nfBQ/view?usp=sharing

Summarize and share your comments here: Failure Prediction using AI/ML in NFV Environments

Both girish and rohit have shared the proposals.

Action: Sridhar to review.

Ex: VM Prediction:

1. VM's Failure Event + Infrastructure (platform) + VM-specific (virtual-Infrastructure) metrics that external to VM.  - Sources are different.
2. VM's Failure Event + Resource-Consumption (Application) metrics that is internal to VM - Sources are same.

Hypothesis: Cadvisor (CMN) metrics  = Collectd (CMN) metrics from Container.

1. EUAG Meeting is yet to happen
2. Request to LF-IT is sent - waiting for response.
3. Work with Pod18 - not yet started. Barometer include Ansible playbooks to deploy collectd+ on K8S.
   1. https://github.com/opnfv/barometer/blob/master/docs/release/userguide/installguide.docker.rst
   2. https://github.com/opnfv/barometer/blob/master/docs/release/userguide/installguide.oneclick.rst

Failure Prediction Definition - Status

(mapping Failures to Data)

• Rohit: Node and VM
• Girish: Container and app

https://docs.google.com/spreadsheets/d/1N9LKZjx117zQHJSLcCFK8dwiOpswWyhZECaNNS6NKHo/edit?usp=sharing

Update this page, if any change is required for the data model: Failure Prediction using AI/ML in NFV Environments