Elastic-R is a virtual collaborative environment for scientific computing and data analysis in the cloud. It aims to lower barriers to accessing cyberinfrastructures and bridge the gap between existing scientific computing environments (SCEs) and grids/clouds. Elastic-R provides plug-and-play access to computational resources like clusters, grids, and clouds. It includes computational components like R packages, Scilab modules, and toolkits. Users can access these components through computational interfaces like a web-based workbench with plugins. Elastic-R also provides storage, scripting environments, and application programming interfaces to facilitate collaboration and reproducible research.

1. Elastic-R
A Virtual Collaborative
Environment for Scientific
Computing and Data Analysis in the
Cloud
Karim Chine
karim.chine@cloudera.co.uk

2. Scientific Computing Environments
www.scipy.org
http://root.cern.ch
o Open-source (GPL) software environment for
statistical computing and graphics
www.sagemath.org
o Lingua franca of data analysis.
o Repositories of contributed R packages related www.scilab.org
to a variety of problem domains in life sciences,
social sciences, finance, econometrics, chemo
www.wolfram.com
metrics, etc. are growing at an exponential rate.
office.microsoft.com
o R Website: http://www.r-project.org/ www.mathworks.com
o CRAN Task View: http://cran.r-project.org/web/views/
o CRAN packages : http://cran.cnr.berkeley.edu/
o Bioconductor: http://www.bioconductor.org/
o R Metrics: https://www.rmetrics.org/ www.sas.com
www.spss.com

3. The ‘s Success Story
From: John Fox, Aspects of the Social Organization and Trajectory of the R Project, R Journal-Feb 2009

4. Scientific Computing Software, HPC and Usability
"Give me a place to stand,
and I shall move the earth
with a lever"

5. Extract from the NetSolve/GridSolve Description Document
The emergence of Grid computing as the prototype of a next generation cyberinfrastructure for science
has excited high expectations for its potential as an accelerator of discovery, but it has also raised
questions about whether and how the broad population of research professionals, who must be the
foundation of such productivity, can be motivated to adopt this new and more complex way of working.
The rise of the new era of scientific modeling and simulation has, after all, been precipitous, and many
science and engineering professionals have only recently become comfortable with the relatively simple
world of the uniprocessor workstations and desktop scientific computing tools. In that world, software
packages such as Matlab and Mathematica represent general-purpose scientific computing
environments (SCEs) that enable users — totaling more than a million worldwide — to solve a wide
variety of problems through flexible user interfaces that can model in a natural way the mathematical
aspects of many different problem domains.
Moreover, the ongoing, exponential increase in the computing resources supplied by the typical
workstation makes these SCEs more and more powerful, and thereby tends to reduce the need for the
kind of resource sharing that represents a major strength of Grid computing [1]. Certainly there are
various forces now urging collaboration across disciplines and distances, and the burgeoning Grid
community, which aims to facilitate such collaboration, has made significant progress in mitigating the
well-known complexities of building, operating, and using distributed computing environments. But it is
unrealistic to expect the transition of research professionals to the Grid to be anything but halting and
slow if it means abandoning the SCEs that they rightfully view as a major source of their productivity.
We therefore believe that Grid computing’s prospects for success will tend to rise and fall according to
its ability to interface smoothly with the general purpose SCEs that are likely to continue to dominate
the toolbox of its targeted user base.
Arnold, D. and Agrawal, S. and Blackford, S. and Dongarra, J. and Miller, M. and Seymour, K. and Sagi, K. and Shi, Z. and Vadhiyar, S.

6. Elastic-R targets major e-Science use cases
o Lower the barriers for accessing cyber infrastructures.
o Bridge the gap between existing SCEs and grids/clouds
o Help dealing with the data deluge (take the computation to the data)
o Enable collaboration within computing environments
o Simplify the science gateways creation and delivery process
o Provide the building blocks of a user-friendly platform for reproducible computational research
o Provide a cloud-based e-Learning environment for computational sciences and statistical education
o Lower the barriers for using distributed computing, make HTC accessible to all research professionals
o Bridge the gap between different mainstream SCEs and between SCEs and workflow workbenches
o Provide a universal computing toolkit for scientific applications and scalability frameworks.
o Provide a portal for scientific computing on demand, collaboration and computational resources sharing
o Merge on demand HPC capabilities, real time collaboration and social networking

7. Elastic-R is a ubiquitous plug-and-play platform for scientific and statistical computing
Elastic-R lowers the barriers of accessing cyberinfrastructures
Computational Components
R packages : CRAN, Bioconductor, Wrapped C,C++,Fortran code
Scilab modules, Matlab Toolkits, etc.
Open source or commercial
Computational User Interfaces
Workbench within the browser
Computational Resources Built-in views / Plugins / Spreadsheets
Hardware & OS agnostic computing engine : R, Scilab,.. Collaborative views
Clusters, grids, private or public clouds Open source or commercial
free: academic grids or pay-per-use: EC2, Azure
Computational Data Storage
Local, NFS, FTP, Amazon S3, Amazon EBS Elastic-R
free or commercial
Computational Scripts
R / Python / Groovy
On client side: interactivity..
On server side: data transfer ..
Computational Application Programming Interfaces
Generated Computational Web Services
Java / SOAP / REST, Stateless and stateful
Stateful or stateless, automatic mapping of R data objects and functions

8. Elastic-R on Infrastructure-as-a-Service style Cloud

9. Anatomy of an Elastic-R machine instance on Amazon EC2
Heartbeat
Restful WS over SSL

10. Elastic-R portal: single facade to public and private clouds
Public Clouds
Private Cloud

11. Exercise 1: Get started with Amazon EC2
Task 1 : Sign Up For Amazon Elastic Compute Cloud
Open the following link : http://aws.amazon.com/ec2/
Click on and follow the instructions
Task 2: Log on to AWS Management Console
Open the following link http://aws.amazon.com/console/
Choose Amazon EC2 in the Combo box and click on
Task 3: Run an Ubuntu Machine Instance
Select the AMIs Panel, Select « All Images », enter the following AMI ID : ami-1437dd7d
Right-click on the unique line displayed and choose « Launch Instance »
AMIs Panel

12. -In the « Request Instances Wizard », click on « Continue » twice
-At the « CREATE KEY PAIR » step, choose « Proceed without a Key Pair » and click on « Continue »
-At the « CONFIGURE FIREWALL » step, choose « Create a new Security Group », fill in the name and the
Description (with a name of your choice) and click on « Continue »
-At the final step « REVIEW », click on « Launch » then click « Close »
-Open the « Instances » Panel, wait for the Status icon to change from orange to green
Task 4: Shut down the Machine Instance
- Right-click on the newly created instance, choose « Terminate » and confirm (« Yes, terminate »)

13. Exercise 2: Get started with Elastic-R
- Open The following link : www.elastic-r.org
- Log on using the login/password you received by email before the tutorial
Alternatively use one of the following accounts
login:sc10-1/passwd:sc10-1 login:sc10-2/passwd:sc10-2
login:sc10-3/passwd:sc10-3 login:sc10-4/passwd:sc10-4
-In the « Cloud Console » panel, provide your Amazon.com login and password
- Keep the default values for machine : « R 2.11.0 Ubuntu 10.04 Lucid – 32 bits », keep the default value for
capacity : «Small–32 bits–1 core–Memory 1.7 G», keep Disk to «None» and provide a label for the machine
- Click on « Launch New Machine »

14. -The Console displays « Retrieving AWS Keys.. » then « Launching.. » then « Machine started successfully …»
-Click « ok » and wait for a new line to appear in the list of Available Machines (with the label you provided)
-When the machine is ready, you get connected automatically, you can now interact with the R session using
the R console and the R graphics

15. -Type the following expressions in the R console :
x=rnorm(100)
var(x)
plot(x)
hist(x)

16. - Open the menus and views and try the different available features

17. Elastic-R is a collaborative Virtual Research Environment.
Users can share their machine instances, stateful remote engines, data,..

18. The Elastic-R portal itself is an EC2 machine instance. Any number of
portals can be run on EC2 for decentralized and private collaboration
Amazon Virtual Private Cloud
Subnet 2
Subnet 1
Subnet 3

19. Exercise 3: Share a machine instance and collaborate with
other attendees.
-Open the collaboration Menu
- Enter the list of Elastic-R
users logins you will share your
machine with in « Sharing
Rights Editor » and click on
« Refresh »
-Providing only logins means
granting all possible rights on
all available engines. Specific
sharing rights can be appended
to the login
-Examples:
john:rw , allow user john to
view and update
peter:r:EngineTest, allow user
peter to view only the session
data on the engine EngineTest
- The new sharing rights
appear in the « Machine
Sharing Rights » panel

20. -Your collaborators can see your machine in their lists of available machines and can connect to it
-Try the different collaboration features(consoles broadcast, graphic annotation, whiteboard, spreadsheets,..

21. -Open the Collaboration Menu and click on “Lead”, change the layout of your environment (resize menus,
open and close views). Your collaborators’ browsers follow your Ajax workbench layout

22. Users can create easily Java User Interfaces (plugins) that use the full
capabilities of a stateful and remote R engine and share them as URLs
Elastic-R AJAX Workbench
Visual Graphic User Interface Builder
Upload plugin
Standalone Application Accessible Elastic-R Java Workbench
From a URL
Plugins Repository
• myPlugin
• myDashboard

23. Software+Services=Applications convergence + ubiquitous collaboration.
The server-side toolkit: R + spreadsheet models + virtual gui widgets.

24. Reproducible research: A scientist can snapshot her computational environment and
her data. She can archive the snapshot or share it with others.
Elastic-R Amazon Machine Images
Elastic-R
AMI 1
R 2.10 + Elastic-R Elastic-R AMI
AMI 2 2
BioC 2.5
R 2.9 + BioC R 2.9
2..3 +
Elastic-R Elastic-R BioC 2.3
AMI 3 EBS 4
Data Set
VVV
R 2.8+BioC
2.0
Amazon Elastic Block Stores
Elastic-R.org
Elastic-R Elastic-R AMI
EBS 4 Elastic-R
EBS 1 2
Data Set
VVV Data Set XXX
R 2.9
+
Elastic-R Elastic-R Elastic-R BioC 2.3
EBS 2 EBS 3 EBS 4
Data Set Data Set Data Set
YYY ZZZ VVV

25. Exercise 4: Create collaboratively and distribute a scientific dashboard
using the Elastic-R server-side widgets designer
- Open The « Views » Menu and check « R Panels », a java applet is loaded
- Right-click on the empty panel, choose “New Slider”
- Keep the default R expression : slider.make("n", x=93,y=15,panel="ssprimary") and click “Apply”
- A slider appears, change the value of the slider and check the value of the R variable “n” in the Console
- Change the value of n
using the R console, and
notice the change in the
slider : the slider is a
Bidirectional mirror of n
-Right-click on the panel
And choose “New Chart”,
keep the default expression
and click “Apply”
-A Bar Chart Mirroring the
R variable “y” appears
-Right-click on the Panel,
Choose “New Macro”, keep
the default expression: the
Macro is triggered when “n”
changes and updates “y”
as being a function of n
-Change the slider and notice
the chart update

26. -Click on « Show Direct URL » and copy the displayed URL to the clipboard
-Open a new Browser Window an paste the url in the browser’s address bar
-Notice the java applet being loaded and the interactive standalone dashboard
-Send the URL by email to you neighbour

27. Exercise 5: call Elastic-R cloud engines from Office documents, mirror an R-enabled
server-side spreadsheet to Excel (Windows users only).
Exercise 5 bis: Install R packages, generate variables, create new files, spreadsheets
and virtual widgets’ panels. Snapshot your environment and share it with others.
- Open the « Tools » menu an click on the Word icon, choose Open Document with Word, read
the displayed message.
-Inside the word document, type an R expression, select it and press Alt-F1.
-Type an R expression producing a graphic, select it and press ALT-F1.

28. -Open the « Tools » menu an click on the Excel icon, choose Open Document with Excel
- Open the « Spreadsheet » view in the browser. Update the cells in the browser, notice what
happens in Excel, update the cells in Excel and notice what happens in the browser

29. The scientist can control any number of stateful R engines from within an R session on
the cloud or on his machine. He can use them for parallel computing

30. Exercise 6: Run two 64-bit Elastic-R machine instances with 4 engines each. Use the 8
engines from within a main R session to apply a function to a large dataset.
-Open the « Portal Info » Menu and choose profile « expert »
-In the cloud console view, enter : your amazon login, your amazon password , “R.2.11.1 – Ubuntu 10.04 – 64
bits”, “Large-64 bits-2 cores-..”, Shutdown after “1” Hour, Disk “None”, empty label, Number “2”, Engine
Names “A,B,C,D”, Memory Min “1024”, Memory max “1024”, check “Use SSL”, click “Launch New Machine”

31. - Select the two new machines and click on « Get Workers Links »

32. - In the R console, notice the R expressions for the Rlinks creation and the Rlinks successful creation
notification
-Type the following R commands in the R console:
>R
To display the vector of Rlinks mapping all the engines in the two Large instances
>rlink.console(R[1], 'ls()')
To send the expression ‘ls() ’ to the R engine referenced by R*1+
>rlink.put(R[1],'n')
To send the variable n to the first engine
> cl<-cluster.make(R)
To create a logical cluster holding all the R engines in the two large instances
>cluster.console(cl, ‘ls()’)
To send the expression ‘ls()’ to the consoles of all the engines
>library(vsn)
>data(kidney)
>l=list()
>for (i in c(1:100)) l[[as.character(i)]]=kidney
To build a list of 100 item, each holding an
ExpressionSet.
>cluster.console(cl, 'library(vsn)')
To load the library vsn on all workers
>cluster.apply(cl, ‘l’, ‘justvsn’)
To normalize the 100 ExpressionSet in parallel
Using the 8 engines.

33. Links
Elastic-R Portal :
www.elastic-r.org
Articles about the project:
Chine K. (2010). Open Science in the Cloud: Towards a Universal Platform for Scientific and
Statistical Computing. In Handbook of Cloud Computing. (Chapter 19). Springer US.
Karim Chine, "Learning Math and Statistics on the Cloud, Towards an EC2-Based Google Docs-like
Portal for Teaching / Learning Collaboratively with R and Scilab," icalt, pp.752-753, 2010 10th IEEE
International Conference on Advanced Learning Technologies, 2010
Karim Chine, "Scientific Computing Environments in the age of virtualization, toward a universal
platform for the Cloud" pp. 44-48, 2009 IEEE International Workshop on Open Source Software for
Scientific Computation (OSSC), 2009
Karim Chine, "Biocep, Towards a Federative, Collaborative, User-Centric, Grid-Enabled and Cloud-
Ready Computational Open Platform" escience,pp.321-322, 2008 Fourth IEEE International
Conference on eScience, 2008
Linkedin Group:
http://www.linkedin.com/groups?home=&gid=2345405

34. Acknowledgments
ACS: Madi Nassiri Amazon: Simone Brunozzi, Deepak Singh AT&T Research Labs: Simon Urbanek ATUGE: Imen Essafi, Béchir
Tourki, Ilyes Gouja, HatemHachicha, Amine Elleuch Auckland Centre for eResearch: Nick Jones Banca d'Italia: Giuseppe Bruno
Bio-IT World: Kevin Davies BNP Paribas: Ousseynou Nakoulima Cambridge Healthtech Institute: Cindy Crowninshield City
University of New York: Mario Morales, Makram Talih Columbia University: Omar Besbes Dassault Systèmes: Omri Ben Ayoun,
Patrick Johnson Dataspora: Michael E. Driscoll EDF: Alejandro Ribes EBI: Alvis Brazma, Wolfgang Huber, Kimmo Kallio, Misha
Kapushesky, Michael Kleen, Alberto Labarga, Philippe Rocca-Serra, Ugis Sarkans, Kirsten Williams, Eamonn Maguire EPFL:
Darlene Goldstein ESPRIT: Farouk Kammoun, Tahar. Benlakhdar e-Taalim: Nadhir Douma ETH Zürich: Yohan Chalabi, Diethelm
Würtz, Martin Mächler European Commission: Konstantinos Glinos, Enric Mitjana, Monika Kacik, Ioannis Sagias FHCRC: Martin
Morgan, Nianhua Li, Seth Falcon Google: Olivier Bosquet FVG LLC: Lisa Wood Harvard University: Tim Clark, Sudeshna Das,
Douglas Burke,Paolo Ciccarese IBM: Jean-Louis Bernaudin, Pascal Sempe, Loic Simon, Lea A Deleris, Alex Fleischer, Alain Chabrier
Imperial College London: Asif Akram, Vasa Curcin, John Darlington, Brian Fuchs Indiana University:Michael Grobe INRIA: David
Monteau, Christian Saguez, Claude Gomez, Sylvestre Ledru JISC: John Wood, David Flanders Johnson & Johnson - Janssen
Pharmaceutica: Patrick Marichal KXEN: Eric Marcade Lancaster University: Robert Crouchley, Daniel Grose Leibniz Universität
Hannover: Kornelius Rohmeier LIAMA: Baogang Hue, Kang Cai Limagrain: Zivan Karaman Mekentosj: Alexander Griekspoor,
Matt Wood Microsoft: Eric Le Marois, Tony Hey Mubadala: Ghazi Ben Amor Nature Publishing Group: Ian Mulvany, Steve Scott
NCeSS: Peter Halfpenny, Rob Procter, Marzieh Asgari-Targhi, Alex Voss, YuWei Lin, Mercedes Argüello Casteleiro, Wei Jie, Meik
Poschen, Katy Middlebrough, Pascal Ekin, June Finch, Farzana Latif, Elisa Pieri, Frank O'Donnell New York Java User Group:
Frank D Greco OeRC: Dimitrina Spencer, Matteo Turilli, David Wallom, Steven Young OMII-UK: Neil Chue Hong, Steve Brewer
OpenAnalytics: Tobias Verbeke Oracle: Dominique van Deth, Andrew Bond OSS Watch: Ross Gardler Platform Computing:
Christopher Smith Royal Society: James Wilsdon San Diego Supercomputer Center: Nancy R. Wilkins-Diehr Sanger Institute: Lars
Jorgensen, Phil Butcher Shell: Wayne.W.Jones, Nigel Smith Société Générale: Anis Maktouf Stanford University: John Chambers,
Balasubramanian Narasimhan, Gunter Walther SYSTEM@TIC: Karim Azoum Technische Universität Dortmund: Uwe Ligges,
Bernd Bischl Technoforge: Pierre-Antoine Durgeat Tekiano: Samy Ben Naceur Télécom-ParisTech: Isabelle Demeure, Georges
Hebrail, Nesrine Gabsi The Generations Network: Jim Porzak Total: Yannick Perigois Tunisian Ministry of Communication
Technologies: Naceur Ammar, Lamia Chaffai-Sghaier, Mohamed Saïd Ouerghi, Syrine Tlili Tunisian Ecole Polytechnique: Riadh
Robbana UC Berkeley: Noureddine El Karoui, Terry Speed UC Davis: Rudy Beran, Debashis Paul, Duncan Temple Lang UCL:
Daniel Jeffares UCLA: Ivo Dinov, Jeroen Ooms UC San Diego: Anthony Gamst UCSF: Tena Sakai Université Catholique de
Louvain: Christian Ritter University of Cambridge: Ian Roberts, Robert MacInnis Peter Murray-Rust, Jim Downing University of
Manchester: Carole Goble, Len Gill, Simon Peters, Richard D Pearson, Iain Buchan, John Ainsworth University of Plymouth: Paul
Hewson University of Split: Ivica Puljak UTK: Ajay Ohri World Bank Group-IFC: Oualid Ammar Yahoo: Laurent Mirguet, Rob
Weltman Independant:Charles Dallas, Romain François