This document discusses using machine learning and artificial intelligence for workload analytics and placement within a cloud environment. Specifically: - The author is researching using machine learning for infrastructure architecture design and experimenting with crowd-sourced telemetry data for trend analysis, pattern matching, and predictive modeling. - The problem statement asks if cloud architecture design can be optimized using crowd-sourced system data. - The document provides an overview of using a machine learning engine to develop data models and visualize data to recommend optimized solutions for sizing and placement of workloads in the cloud.

1. Machine Learning (AI) for workload analytics
and placement within a cloud environment
Kenneth Moore

2. © Copyright 2017 Dell Inc.2
Kenneth Moore
Data and Function Engineering
Dell EMC, Converged Platforms and Solutions Division
• Researching Machine Learning / Artificial Intelligence for
infrastructure architecture design
• Experimenting with crowd sourced telemetry data sets for
trend analysis, pattern matching and predictive modelling
• Multiple patents pending in this topic

3. © Copyright 2017 Dell Inc.3
Problem Statement
Can we optimise cloud architecture design
using crowd sourced system data?

4. Virtualisation does optimise infrastructure…
Virtualisation tool == production environment
Data from virtualisation tool usage == design environment optimisation

5. The Goldilocks Principle
Learning and understanding our customer base
• Goldilocks asked for porridge
– What does that really mean?
– Was temperature part of her request
• We’ve learned other details from this experiment
– Similarities between goldilocks and baby bear
– We know what daddy, mammy and baby bear like
• Now, Can we provide porridge for a new
character, e.g. uncle bear
– What has he asked for?
– Who is his closest match?
Too Hot Too Cold Just RightJust Right Just Right Just Right

6. Starting point !!!

7. Exercise objective
Shopping List
Modelling
Environment
Recommended
Solution

8. Developing our modelling environment

9. Machine learning engine
Data Models
Statistics
Outliers
Clusters
Normal
Distribution

10. Data model visualisation
Lookup Tables Mathematical
Formula
Decision Tree
Compressed formats of the original data

11. Sizing SpecificationSizing Specification
Artificial intelligence (AI) modelling

12. Ideal Region
NE quadrant
AI search optimisation
Best Fit
Better FitGood Fit
Cost
Performance
• Normalisation (specification):
Specification attributes calculated as a
represented value of the specification
• Normalisation (directional):
- maximum values = no change
- minimum values = inverse power (-1)
• State space:
Data points represent all potentially
viable solutions
• Ideal model:
Approximates North-East trajectory
• Model heuristic navigation:
- maximum magnitude
- minimum distance from ideal
%
% -1

13. Ideal Region in
NE quadrant
Modeling complexity
Best
Fit
Better
Fit
Good
Fit
Cost
Performance

14. Collect
Design / Test
Adjust / Refine
Repeat / re-educate
/ Analyse
Key take-aways

15. © Copyright 2017 Dell Inc.15
Questions

16. Machine Learning (AI) for workload analytics
and placement within a cloud environment
Kenneth Moore