What the Cloud can do for
Computational Life Sciences:
Biocep-R's Unified Perspective
Karim Chine
karim.chine@m4x.org
www.biocep.net
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..
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
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
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)
free or commercial
Biocep
Computational Scripts
R / Python / Groovy
On client side: interactivity..
On server side: data transfer ..
Generated Computational Web Services
Stateful or stateless, automatic mapping of R data objects and functions
Computational Application Programming Interfaces
Java / SOAP / REST, Stateless and stateful
Biocep-R, Technologie Environment
R Server
R Virtualization
rJava / JRI
JavaGD
Object Export / Import Layer
mapping
RServices API
Server Side - Personal Machine, Academic Grids, Clusters, Clouds
Client Side - Internet
RServices skeleton
Graphic devices skels R packages skels
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
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
Perl Scripts
 logOn
 logOn
 Use R
 Use R
 logOff
 logOff
Node 4 : EC2 virtual machine 1
Node 4 : EC2 virtual machine 1
Web Application
 Borrow R
Cloudbursting
 Generate Graphics/Data
via AWS
 Release R
Node 5 : EC2 virtual machine 2
Elastic distributed computing on Amazon EC2
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.
♦ 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 inhouse 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