Cloud Computing

Acknowledgment

This lecture note is based on Dr. Hua Zhou’s 2018 Winter Statistical Computing course notes available at http://hua-zhou.github.io/teaching/biostatm280-2018winter/index.html.

What is cloud computing

• Using a collection of remote servers for computation, data storage/manipulation, etc.

• Pay for clock-cycles, storage, and network flow rather than hardware.

• Computing when you need it:
  • Scalable computing
  • Scalable storage

It’s not in cloud

Why cloud computing is so popular

• Scalability!

• Adapt to fluctuating demand:

  • Websites with fluctuating traffic

  • large corporations use much more computing during business hours than off-business hours

• Efficiency

  • Pay for what you need

  • No need for hardware maintenance

  • Less waiting for fixed compute-time jobs

Why we care about cloud computing

• Our computational demands often fluctuate dramatically.

• Grant resource management.
  • Old style: buy computer with grant X money, must use computer only for grant X research.
    
  • New style: buy computing time for grant X research with grant X money.
• Massive computing when you need it
  • On the cloud you can run your job on 10,000 machines for one hour for the same price as running it on one machine for 10,000 hours!
  • Using AWS EC2 spot instances, this can be done for $700 an hour, and gets you super-computer performance.
  • It may be more compelling to know that you can get 8 cores for around $0.27 an hour.

Vendors

There are many vendors out there. Good for customers (us). They all work similarly.

• Amazon web services (AWS).

• Google cloud platform (GCP).

• Microsoft Azure.

• IBM cloud.

We will demonstrate how to start using GCP.

Using GCP Compute Engine: basic workflow

0. Set up GCP account.

1. Configure and launch VM instance(s).

2. Set up connection (SSH key).

3. Install softwares you need.

4. Run your jobs.

5. Transfer result.

6. Terminate instance(s).

Step 0: set up GCP account

• Go to https://cloud.google.com.

• Make use you sign in using your Google account. You may want to sign out your existing Google accounts and create a new one before next steps.

• If you click Try it free at https://cloud.google.com and fill out requisite information, you will get $300 credit which expires in 1 year. You do not need to use it for this course. But it’s better to claim the credit before redeeming the coupon below.

• You will be asked for a name and email address.

• After email verification and redeeming the coupon, a project My First Project is created in GCP.

GCP free trial:

• Some resources are always free: 1 f1-micro VM instance, 30GB of standard persistent disk storage, etc.

• General pricing can be found on this page.

Step 1: configure and launch a VM instance

• Go to GCP console, create a project.

• Go to Compute Engine, click CREATE INSTANCE.

  • Give a meaningful name, e.g, snustat-326_621a-2018.

  • Choose asia-northeast-b zone.

  • Machine type: 2 vCPUs, 7.5GB memory should suffice for this course.

  • Boot disk: CentOS 7, standard persistent disk (or SSD) 25GB should sufficie for this course.

  • These settings can be changed anytime. Typical paradigm: develop code using an inexpensive machine type and switch to a powerful one when running computation intensive tasks.

  • Click Create.

• At the VM Instances page, you can see a list of all instances in your project and their IP addresses. We use the external IP address, e.g., 35.187.203.86, for SSH connection.

• Note if we stop the instance and start again. The external IP address may change. To keep a fixed external IP address, go to VPC network then External IP addresses and make the desired external IP address static (vs ephemeral). Note that if no instance is using a static IP address, you will be charged for the idle static IP.

Step 2: set up SSH keys

There are several ways to connect to the VM instance you just created. Most often we want to be able to SSH into the VM instance from other machines, e.g., your own laptop. By default, VM instance only accept key authentication. So it’s necessary to set up the SSH key first.

• Click the SSH button on VM Instances page will bring out a terminal in browser as super user, e.g., johann_won (your gmail account name).

• In that browser, you can set up your own SSH keys:

cd
mkdir .ssh
chmod go-rx .ssh/
cd .ssh
vi authorized_keys

Copy your public key to authorized_keys and set permission

chmod go-rwx authorized_keys

If you don’t have a public key, then you may want try password authentication. To do this, edit /etc/ssh/sshd_config file by

sudo vi /etc/ssh/sshd_config

and uncomment the line

#PasswordAuthentication yes 

Don’t forget to comment out the line

PasswordAuthentication no 

Then, add password to your account by

sudo password $(YOUR_GOOGLE_ACCOUNT_NAME)

Finally, restart the ssh daemon by

sudo service sshd restart

• Now you should be able to connect to the VM instance from any machine with the matching private key:

ssh $(YOUR_GOOGLE_ACCOUNT_NAME)@35.187.203.86    ## actual ip address should be different

Step 3: install software

yum is the default package management tool on CentOS. Most software can be installed via sudo yum. sudo executes a command as a superuser (or root).

Install R/R Studio Server

• Install Extra Packages for Enterprise Linux (if not yet)

sudo yum install epel-release -y
sudo yum update -y

• Install R (it takes a couple minutes)

sudo yum install R -y

• Install wget, which is a command line tool for downloading files from internet.

sudo yum install wget -y

• Download and install the most recent R Studio Server. It is version 1.1.463 as of November 11, 2018.

wget https://download2.rstudio.org/rstudio-server-rhel-1.1.463-x86_64.rpm
sudo yum install rstudio-server-rhel-1.1.463-x86_64.rpm
rm rstudio-server-rhel-1.1.463-x86_64.rpm

• The R Studio service starts immediately. Let’s check if it is running properly by triggering the following command.

sudo systemctl status rstudio-server.service

• By default, port 8787 used by R Studio Server is blocked by VM firewall. On GCP console, go to VPC network and then Firewall rules, create a rule for R Studio Server (tcp: 8787), apply that rule to your VM instance.

• Now you should be able to access R Studio Server on the VM instance by pointing your browser to address http://35.187.203.86:8787.

Set up a regular user

• Key authentication suffices for most applications.

• Unfortunately R Studio Server (open source edition) does not support key authentication. That implies if you want to use R Studio Server on the VM Instance, you need to enable username/password authentication.

• As super user e.g. johann_won, you can create a regular user, say wonj:

sudo useradd -m wonj

The -m option creates the home folder /home/wonj.

• You can set password for a user by

sudo passwd wonj

• Now you should be able to log in the R Studio Server from browser http://35.187.203.86:8787 using username wonj and corresponding password.

• To SSH into VM instance as the regular user wonj, you need to set up the key (similar to set up key for superuser).

• If you want to enable the regular user as a sudoer, add it into the wheel group:

su - johann_won
sudo usermod -aG wheel wonj
su - wonj

Install R packages

• When installing R packages, it often fails because certain Linux libraries are absent.

• Pay attention to the error messages, and install those libraries using yum.

• E.g., try installing tidyverse may yield following errors

ERROR: dependencies ‘httr’, ‘rvest’, ‘xml2’ are not available for package ‘tidyverse’
* removing ‘/home/wonj/R/x86_64-redhat-linux-gnu-library/3.4/tidyverse’

You can install these Linux dependencies curl, openssl, and libxml2 by:

sudo yum install curl curl-devel -y
sudo yum install openssl openssl-devel -y
sudo yum install libxml2 libxml2-devel -y

Install Git

• Install Git on VM instance:

sudo yum install git -y

• For smooth Gitting, you need to put the private key matching the public key in your GitHub account in the ~/.ssh folder on the VM instance.

• Now you can git clone any repo to the VM instance to start working on a project. E.g.,

git clone https://github.com/snu-stat/stat326_621a-2018-hw2

Step 4: run your jobs

• Now you have R and R Studio on the VM instance.

• Simpliest way to synchronize your project files across machines is Git, e.g.,

git clone https://github.com/snu-stat/stat326_621a-2018-hw2

• Set up and run your jobs as usual.

• You can check CPU usage on the GCP console.

• You can set notification when CPU usage falls below a threshold (so you know the job is done).

Step 5: transfer results

• Transfer results as usual: scp, sftp, Git.

Step 6: terminate instance(s)

• Don’t forget to terminate your instance(s). You’ll get charged!

Go forth and use the cloud

• Using cloud (AWS, Azure, GCP, …) is easy.

• Easy to launch cluster instances or other heavily customized instances (SQL server, BigQuery, ML engine, Genomics, …).

• Massive computing at your fingertips.

  • Amazon EC2 P2 instances offer on a single virtual machine 16 GPUs, with 40,000 parallel processing cores, and 732 GB memory, yielding 70 teraflops of single precision floating point performance or 23 teraflops of double-precision.

  

  • GCP recently announced TPU (tensor processing units) for deep learning. Each Cloud TPU offers up to 180 teraflops of computing performance as well as 64 gigabytes of ultra-high-bandwidth memory.

  

  • To put it in perspective, 180 teraflops is equivalent to the most powerful high performance cluster (HPC) in 2005, according to following slide by Jack Dongarra.