The document discusses the integration of open science and cloud computing, emphasizing the importance of cloud-based platforms for mathematical and statistical computing. It highlights tools like R and Scilab for data analysis, their applications in collaborative environments, and the advantages of using cloud infrastructure for scalable computing. The document also mentions the benefits of cloud computing in disaster recovery, real-time collaboration, and improved accessibility to statistical applications.

1. Open Science in the cloud,
towards a universal platform for
mathematical and statistical
computing
Karim Chine
karim.chine@m4x.org

2. www.biocep.net
LinkedIn Group : Elastic-R

3. Definitions
♦ What is the Cloud ?
Cloud computing is a paradigm of computing in which dynamically scalable and often
virtualized resources are provided as a service over the Internet.Users need not have knowledge of, expertise in,
or control over the technology infrastructure in the "cloud" that supports them. Wikipedia
Cloud Computing represents a new way to deploy computing technology to give users the
ability to access, work on, share and store information using the internet. The cloud itself is a network of
data centers- each composed of many thousands of computers working together- that can
perform the functions of software on a personal or busisness computer by providing users access to
powerful applications, platforms and services delivered over the internet.
Jeffrey F. rayport & Andrew Heyward (Marketplace LLC)
♦ What is R ?
Open-source (GPL) software environment for statistical computing and graphics
Lingua franca of data analysis.
Repositories of contributed R packages related to a variety of problem domains in life
sciences, social sciences, finance, econometrics, chemo metrics, etc. are growing at an exponential rate.
♦ What is Scilab ?
Open-source (CeCILL) software package for numerical computations.
Clone of Matlab.
Widely used for engineering and scientific applications.
♦ What is an SCE ?
Scientific Computing Environment : enables users 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. Examples : Matlab, Mathematica, Scilab, R..

4. e-Science perspective / Biocep-R use cases
♦ Lower the barriers for accessing cyber infrastructures.
♦ Help dealing with the data deluge (take the computation to the data)
♦ Enable collaboration within computing environments
♦ Simplify the science gateways creation and delivery process
♦ Bridge the gap between existing SCEs and grids/clouds
♦ Lower the barriers for using distributed computing, leverage the elastic cloud

5. e-Science perspective / Biocep-R use cases
♦ Bridge the gap between mainstream SCEs
♦ Bridge the gap between mainstream SCEs and workflow workbenches
♦ Provide a universal computing toolkit for scientific applications
♦ Provide frameworks for computational back-ends scalability
♦ Provide the building blocks of a platform for computational education
♦ Provide the building blocks of a traceable and reproducible
computational research platform
♦ Provide the building blocks of an international portal for scientific
computing on demand, collaboration and computational artifacts/resources sharing

6. Computational Ecosystem, "The" Open Platform
Computational Components
R packages : CRAN, Bioconductor, Wrapped C,C++,Fortran code
Scilab modules, Matlab Toolkits, etc.
Open source or commercial
Computational User Interfaces
Virtual workbench within the browser
Computational Resources Built-in views / Plugins / Spreadsheets
Hardware/OS agnostic computing engine : R, Scilab,.. Collaborative views
Clusters, grids, cloud servers Open source or commercial
free: academic grids (NGS, EGEE, etc.) or pay-per-use: EC2
Computational Data Storage
Local, NFS, FTP, Storage Web Services (S3) Biocep
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

7. Biocep-R, Technologie Environment

8. R Server
R Virtualization
rJava / JRI JavaGD
Object Export / Import Layer mapping
RServices API
RServices skeleton Graphic devices skels R packages skels
Server Side - Personal Machine, Academic Grids, Clusters, Clouds
Client Side - Internet
Virtual R Workbench
Internet Browser
Java Applet
Virtual R Workbench URL
Docking Framework
R Console
R Graphic Device+Interactors
R Workspace
R Help Browser
R Script Editor
R Spreadsheet
Groovy / Jython Script Editor

9. Computational Engines Pools / cloudbursting Pool A
Pool B
Pool C
Node 1: Windows XP
Node 2: Mac OS
Front-end host
Remote Objects
Registry
R-HTTP R-SOAP Node 3: 64 bits Server / Linux
Parallel Computing
Applications
→ Borrow Rs
Supervisor
→ Use Rs
→ Release Rs
.NET Appli Node 4 : EC2 virtual machine 1
Perl Scripts Node 4 : EC2 virtual machine 1
→ logOn Web Application
→ logOn
→ Use R → Borrow R Cloudbursting
→ Use R
→ logOff → Generate Graphics/Data
→ logOff via AWS
→ Release R
Node 5 : EC2 virtual machine 2

10. Elastic distributed computing on Amazon EC2

11. Shell’s Biocep-R-based statistical modelling
cloud computing pilot
Extracts from Shell’s cloud computing big rules document :
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The Global Solutions statistics group actively uses the open source “R” statistical
modeling tool. An inexpensive platform upon which to run the statistical models was
required with the ability to scale up and down depending on calculating demand.
In order to achieve this, the pilot created an analytical application using a pool of
stateless and, more importently, statefull “R” engines across multiple servers in Amazon
using Biocep for integration and virtualisation of the “R” engine.
Using Amazon enabled them to have
♦ On-demand access to high-powered computing facilities. Numerically intensive
statistical applications can be handled by the cloud rather than slowing down the users
own PC. Could be of great benefit in the Bio-Fuels research area, which will require very
computationally intensive statistical techniques.

12. ♦ Disaster Recovery: By using virtual machine images on the cloud we can always
restore to the initial state. If something goes drastically wrong with the cloud machine
image we can simply scrap it and launch another instance. Safer to implement web apps
on a virtual machine using AWS rather than in-house server.
♦ The Cloud can be used as a real-time collaborative workspace. Co-workers can work
together and share statistical methodologies in a new and novel environment.
♦ The onset of Cloud Computing has greatly increased the availability of software for
delivering web-based statistical applications. The benefits of which include:
o No special configuration or changes are needed on users PCs.
o No need for scripting of applications.
o Compatible with all operating systems.
o Updates can be made quickly and easily in a centralized manner.
o Everybody has a browser. Familiar interface encourages use.
o Statistical web-based applications can either be hosted on the cloud or an in-
house Shell server: which may be more appropriate for most confidential data.
>
Contacts within Shell :
Edwin Vansteenis, Shell Global Functions, Senior IT Architect, edwin.vansteenis@shell.com
Wayne W. Johnes, Shell Global Services, Statistical Consultant, Wayne.W.Jones@shell.com