The document discusses the adoption of cloud computing in scientific research, highlighting its potential to provide customizable, on-demand computing resources for data processing and analysis. It compares cloud computing with other distributed computing paradigms, outlines various domain-driven solutions, and addresses challenges such as trust, data ownership, and software licensing. Despite the benefits, the overall adoption in academia remains limited due to various barriers including perception issues and specific application beliefs.

1. Adoption of Cloud Computing
in Scientific Research
Yehia El-khatib
School of Computing & Communications
Lancaster University

2. Obligatory cloud image…

3. Outline
• Cloud Computing in Business
• Cloud Computing in Research
– What does it offer
– Comparison with other distributed paradigms
– Different solutions
– Examples
– Challenges
• Conclusions

4. Cloud Computing
• Computational and storage resources provided in an
on-demand fashion by large clusters of commodity
computers.
• Offers opportunities:
– Customised and isolated computing resources are
obtained as and when required to handle user demand.
– Pay per use model allows feasibility and sustainability
through harnessing economies of scale.
– Management via web service APIs.
– Universal Internet-based access (all you need is / / / / … ).

5. Cloud Computing in Business
• Used to curb computing expenses without
restricting the business.
– Scale  to meet user demand.
– Dynamically mitigate system failures.
– Seamlessly roll out new capabilities.
• Numerous users:
• Cloud computing market
– Worth $40.7bn in 2010
– Expected $177bn in 2015
– Expected $241bn in 2020
 http://www.forrester.com/rb/Research/sizing_cloud/q/id/58161/t/2
 http://www.gartner.com/it/page.jsp?id=1735214

6. Academic Research
• Researchers do not spend their
entire time in the lab, field, etc.
• Collected data needs to be
processed in order to distil some
meaning.
• Such analysis processes range from
scripts and spreadsheets to very
complex computationally-intensive
workflows.
• More data is being gathered using
innovative methods (e.g. remote
sensing).

7. Cloud in Academia
• People in academic circles are slowly adopting
cloud computing for particular applications.
• What does the cloud offer?
– ‘Everything as a service’ promotes integration and
relatively easy collaboration across institutions,
communities and disciplines.
– Customised environments.
– Elastic computing infrastructure.
– More load off the users, i.e. scientists.
 More time to focus on their scientific processes.

8. Distributed Computing Paradigms
HPC Grid P2P Cloud
Ownership
My university Our universities Our partners 3rd party
(management)
 Trust in
Trust Very High High ?
partners
Depends on
Reliability High High Very high
size & partners
Individual &
Individual
Accounting Organisational Difficult… Pay per use
Quotas
quotas
Customisation Very bad Bad Fairly flexible Very flexible
Access Easy Complicated Complicated Easy
Local Remote Local/Remote
Support 24x7 support
sysadmin sysadmin sysadmin

9. What solutions do clouds offer?
• Generic solutions: Research Support
– Infrastructure (e.g. EmuLab)
– Analysis (e.g. Biocep-R, CloudNumbers)
– Space to discover (e.g. Academia.edu), share (e.g.
myExperiment) and collaborate (e.g. Mendeley)
• Domain-driven solutions:
Research
– Workflow execution
– Data normalisation
– Data discovery, based on content rather than
problem area

10. Domain-driven Cloud Solutions
• Environmental Virtual Observatory pilot
(EVOp)
http://www.EnvironmentalVirtualObservatory.org
– To help:
• Environmental scientists solve ‘big questions’.
• Policy makers understand implications of decisions.
• Raise awareness in and interact with local communities.
– Use case for pilot phase: hydrology.
– Deal with both geospatial and time series data.
– Customisable modelling workflows for scientists.
– Predefined analysis tools for non-specialists.

11. Domain-driven Cloud Solutions
• Penn State Integrated Hydrologic Model (PIHM)
http://slidesha.re/pFFMWp
– Terrestrial watershed modelling in order to predict
water distribution.
– Data is sourced through a repository.
– Cloud offers seamless access to abundant
resources to carry out modelling workflows and
simulations.
– Results are delivered using bespoke visualisation
(SaaS).

12. Domain-driven Cloud Solutions
• Coaddition of SDSS Astronomical Images
http://arxiv.org/abs/1010.1015
– Using Apache Hadoop for coaddition of images from
the Sloan Digital Sky Survey. (Coaddition increases the
signal-to-noise ratio).
– Runs over NSF cloud maintained by Google and IBM.
– Experimented different approaches to coaddition
using the MapReduce framework.
– Improved performance was achieved by reducing job
initialisation overhead using index files.
– 300 million pixels processed in 3 minutes.

13. Domain-driven Cloud Solutions
• Cell structure analysis http://books.google.co.uk/books?id=C_aQqAa6rEoC
– Hadoop jobs to analyse videos of single cell structures
under varying conditions.
• European Space Agency http://www.esa.int
– Uses AWS EC2 & S3 to deliver data about the current state
of the planet to scientists, governmental agencies and other
organizations worldwide.
• MD Anderson Cancer Center http://bit.ly/o0zDwl
– Large private cloud (8,000 processors) maintained by The
University of Texas.
– Used to execute genomic processes against large clinical
datasets (~1.4PB) on cancer.

14. Domain-driven Cloud Solutions
• NSF http://www.nsf.gov/news/news_summ.jsp?cntn_id=119248
– Approx. $4.5m to fund 13 research projects.
– Mostly CS, but also bioinformatics & earth sciences.
• VENUS-C http://www.venus-c.eu
– 15 year-long pilots in different disciplines: architecture,
biology, bioinformatics, chemistry, earth sciences, healthcare,
maritime surveillance, mathematics, physics and social media.
• Masters @ SCC Lancaster
– Corpus linguistics
– Hydrological modelling
– 3D imaging (volcanology)

15. Challenges
• Trust: security and privacy (even by law in some
circumstances).
• Great divide between different disciplines.
• Data ownership.
– Most data producers don’t mind sharing as long as they
retain ownership.
• Software licenses.
• Belief that cloud/grid/etc is only for certain app’s.
• Investment into delivering cloud-based solutions
to scientists.
– Legacy applications & infrastructures.

16. Challenges
• Trust: security and privacy (even by law in some
circumstances).
• Great divide between different disciplines.
• Data ownership.
– Most data producers don’t mind sharing as long as they
retain ownership.
• Software licenses.
• Belief that cloud/grid/etc is only for certain app’s.
• Investment into delivering cloud-based solutions
to scientists.
– Legacy applications & infrastructures.

17. Conclusions
• Need for cloud computing for scientific research:
– Mainly: “I need more number crunching!”
– Also: “I need to bridge data/discipline gaps.”
• Overall adoption is still relatively limited.
– Various reasons, including trust. But also cloud-unrelated
problems such as data ownership and software licensing.
• Investment into cloud-enabled research is important.
– Not to browse articles via a mobile app while on the tube…
– But for the added value of building and nurturing
relationships.
– And the economic model (less up front costs).
• Impact:
– Better scientific tools, with less overhead on the scientists.
– Potential for more integration.

18. Thank you!
Questions
Flickr credits:
• theaucitron • stacylynn
• theplanetdotcom • bpamerica
• Pnnl • soilscience
http://www.comp.lancs.ac.uk/~elkhatib/
Yehia El-khatib @yelkhatib
http://www.EnvironmentalVirtualObservatory.org
@EVOpilot

19. Discussion
• Trust is not the problem; it is the perception of trust.
• Different academic communities have varying attitudes
towards new technologies such as the cloud.
• More examples of funding to adopt cloud computing:
o research: http://www.jisc.ac.uk/news/stories/2011/02/umf.aspx
o Gov’t: http://www.cabinetoffice.gov.uk/content/government-ict-strategy