Cloud 101: Introduction

Cloud adoption framework


Teaching: 20 min
Exercises: 0 min
  • What is our cloud framework?

  • How does this map to my research?

  • Understand the cloud framework we present

  • Map your personal perspective and objectives to this course


This course is is open: Publicly available and we have tried to make it self-guided as much as possible. If you are working on your own you will need to set up and configure cloud accounts with up to three vendors/platforms that we discuss: Amazon Web Services, Microsoft Azure and Google Cloud Platform. In all cases you can get a small test account (with credit on the order of a couple hundred dollars) but you may be obliged to provide a credit card.

Why Are We Here?

If cloud computing was just Virtual Machines and Storage we would not be here today! There must more going on; and our first evidence is the appeal of this course across research domains. The first class included 45 attendees with representatives from Oceanography, Libraries, Biology, eScience, Forestry, Bioinformatics, Sociology, Computer Science, Hospitals, Environmental Science, Astronomy, Electrical Engineering, the Information School and King County Metro!

red queen

We work out of the eScience Institute and UW IT to try and accelerate research by helping the Researcher. Our model for the Researcher is the Red Queen from Through The Looking Glass: A person running very fast just to stay in once place. We tend to work with Researchers who can make the time to stop running for a moment in order to learn about and evaluate the cloud as a research computing platform. This one-day course is primarily introductory but also as hands-on as we can make it.

Our Call To Action To You

Our call to action to you: Share awareness of both this course and of the eScience Institute with your colleagues.

Your way foreward after today

What this course is

Challenge (click arrow to the right to open)

What it isn’t

Two severe weather advisories

The burden of cloud management is on each of us

There is considerable detail to learn about managing your work on the public cloud. Without this skill life can quickly become expensive; for example if you accidentally allocate > expensive resources and leave them runningi. (Cloud instances can be turned off without losing state/progress and they can be saved as memory images.)

Lemons from lemonade

A good way of getting some bad news is to publish and then delete your cloud access credentials on GitHub. GitHub supports versioning: Someone who is not your friend can roll back your public repository to the version where the key was present, grab that key, and start using your cloud account at your expense.

Our Public Cloud Framework

This framework is the vocabulary and relationships we use to describe using the public cloud platform for data-driven research. Here comes the jargon storm! In what follows we assume you are a Researcher focused on data-driven science and that you are interested in adopting the cloud as a way of streamlining that process in some capacity.

As a Researcher you do perfunctory processing and exploratory processing. The cloud can help you with both, but at a cost: The time you invest to learn new methods. We call this cloud adoption and the core premise is that you no longer have a familiar computer with an attached storage system where you log in and do your work. The cloud model is (they like to say) cattle not pets: You have a huge pool of available compute resources and you rent them by the hour. When you are done with them you simply Stop or Terminate them and they go back into the resource pool. Before continuing let’s do a quick cost analysis of what this means: How does a cloud machine compare to a desktop?

A Cloud Under Your Desk

A good desktop might cost $3000; and a very powerful cloud instance will cost about $0.40 (USD) per hour. Let’s say for the sake of argument that they are equivalent in compute power and attached storage. If you work eight-hour days with four weeks of vacation then your annual compute cost is roughly $800. Over three years your “cloud under the desk” runs you $2400; but you can make this cheaper if you do not need the compute power; or you can throttle it up when you need a lot. You might also ask: What are the additional tradeoffs and other factors? Rather than try to spell these out we refer you to the cloudmaven website and to our office hours for consulting: The subject is too complex to address here; but to first order renting a cloud machine and turning it off every night can be quite cost-effective. One thing we will mention is that when you come in in the morning and start your cloud machine you will have a couple minutes to go get some coffee.

More generally: Cloud adoption is awareness + learning + matching the technology to the details of your research. If you consider the pie chart of your time sliced into activities: Our goal is to reduce the slices that are detracting from your write and publish papers slice. We can also introduce you to new things like publishing your data and your software; cf the executable paper.

The cloud components are compute, store, manage, web and services

The cloud facets to learn about are admin, cost, security, scale and time

% sudo yum update

Should I move to the cloud?

This really depends on the value of your time in relation to your research budget; and on how much of your wall clock time you spend doing computing. It also depends on your team’s capacity to assess and learn cloud tech for your work. This might be very fast - which is what we find in the majority of cases - but if you are getting into sophisticated work e.g. using a web framework then there could be a substantial bootstrapping effort required.

Our call to action is: ‘Learn enough to learn enough to learn enough… to evaluate cloud migration.’ The simplest approach is to visit us by appointment or during drop-in office hours.

Which Cloud Should I Use?

“It depends…” and here are some factors.

Category AWS Azure Google
vCPU hour $0.01 $0.01 $0.01
storage GB-month 0.024 0.030 0.030
5900 vCPUs x 53 hours 3400 unclear $4000
DBaaS per month what evil lurks
Egress < 15% of MB 0 0 unknown
attached storage GB-month 0.10 unknown unknown

Questions For Discussion

This project is funded in part by NSF Campus Cyberinfrastructure - Infrastructure, Innovation and Engineering Program (CCIIE) Grant Aware No. ACI-1440281; Principal Investigator Brad Greer, CTO, UW-IT.*

Key Points