Adoption of Cloud Computing in Scientific Research
Some might say the scientific research community is somewhat behind the curve of adopting the cloud. In this talk, I present a few examples of adopting the cloud from the wider research community. I also highlight some of the aspects by which cloud computing could affect scientific research in the near future and the associated challenges.
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 quotasCustomisation 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 dont mind sharing as long as they retain ownership. Software licenses. Belief that cloud/grid/etc is only for certain apps. 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 dont mind sharing as long as they retain ownership. Software licenses. Belief that cloud/grid/etc is only for certain apps. 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! QuestionsFlickr 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 Govt: http://www.cabinetoffice.gov.uk/content/government-ict-strategy