Stuart Stafford of estrat provides an overview of the "art" of migrating workloads into AWS.

1. Vision | Traction | Results

2. Welcome
Stuart Stafford – Chief Technology Officer

3. The Art of Workload Translation
How it works in the real world!

4. Agenda
WHAT IS WORKLOAD TRANSLATION?
FIRST, KNOW YOURSELF
THINKING DIFFERENTLY
IDENTIFYING THE OPPORTUNITIES (AND THE LIMITATIONS)
TOP TIPS
Q&A

5. What is Workload Translation?
• The process of adapting a workload from one infrastructure paradigm
to another.
• Involves revisiting architecture and sizing decisions from a different
perspective using different criteria.
• Not solely a technical exercise.
• Can be whole of environment or a point solution.
• Art vs Science.

6. Paradigm Change (Airline vs Passengers)
Typical On-Premise Cloud
Big chunk sunk costs Fine grained consumption costs
Manual processes Highly automated
Full access (Perceived freedom) Operational control (Perceived restrictions)
Enduring assets Disposable Resources
BAU focussed Value focussed
Components Services

7. The Wider View
• Technical differences.
• Financial model.
• Skills requirements.
• Vendor support.
• Licensing
• Business and legislative requirements.

8. Art vs Science
• Tools and calculations are valuable – to a point.
• Hands on experience and observation is crucial.
• Challenge the numbers.
• Business drivers, available skills and other “soft” factors can override
technical considerations.
• An element of judgement is always required.

9. First, know yourself!
• How well do you know your existing environment?
• The right information reduces the risk and improves the outcome.
• “Pay for use” actually means “Pay for what you turn on even if you
don’t use it”. What aren’t you using?
• Estimation is often required.
• Remember to think about business priorities and objectives.

10. Instance Sizing
• Minimum you need Peak, Average and “Profile” of the following per
instance;
• CPU (Mhz).
• Disk IO per volume (IOPS).
• Network Throughput (MBPS).
• Understand the characteristics of each instance and the software
within (Background processing, Real time user responsiveness,
memory hogs).
• Measure at the virtualisation layer.
• Know where the resource constraints are skewing the numbers.

11. Metric Collection example

12. EC2 Instance selection

13. Storage sizing
• IOPS is the starting point (Peak, Avg and Profile) but don’t ignore
throughput.
• Cross reference with latency to identify existing constraints.
• Beware the backups.
• Adjust the numbers if required.
• Understand the relationship between the storage performance of a
volume and the workload delivered by it.

14. EBS Volume Selection

15. Network sizing
• vNic stats are only the starting point.
• Whole environment Netflow style data is the Nirvana.
• Know where it goes and what it does.
• Beware the backups.
• Think about EC2 Instance type network performance.
• Consider latency requirements.

16. Extra things to consider
• Vendor support / requirements.
• Backup / data protection.
• Network reliability / resilience.
• Changes to application architecture.
• Monitoring.
• Licensing.

17. Thinking Differently
• Logical vs Physical .
• Designing for failure.
• Run it hot (What you need and no more) where you can.
• Services vs DIY.
• Micro cost modelling.
• Licensing by the hour.

18. Micro cost modelling

19. Identifying the Opportunities
• Cost reduction (looking at the whole picture).
• Technical debt reduction (Offload the work).
• Making the intangible, tangible (true costs for systems).
• Flexibility, Disposability and Repeatability.
• Environment wide variability (Performance / Resiliance /
Redundancy).
• Culture change (Shift from technology focus to business focus).
• Virtually unlimited infrastructure.

20. Identifying the Limitations / Traps
• Visibility of the hardware (Actually a benefit…).
• Lack of access to the hypervisor.
• Lack of VM console access to EC2 instances.
• No live migration of EC2 instances.
• Stuff still breaks.
• When it’s gone, it’s gone.
• Bill shock.

21. Top Tips
• Put standards and discipline in place day one.
• Tackle “easy” use cases first.
• Combine statistics with observations to really understand workloads.
• Stage architecture changes to manage risk.
• Read the detail on the AWS services.
• Keep focus on business drivers.

22. Q&A
Thank You!