Accelerating data-intensive s cience by outsourcing the m undane Ian Foster

1. Accelerating data-intensive science by outsourcing the mundaneIan Foster

3. MACHO et al.: 1 TB Palomar: 3 TB 2MASS: 10 TB GALEX: 30 TB Sloan: 40 TB Pan-STARRS: 40,000 TB The data deluge 100,000 TB Genomic sequencing output x2 every 9 month >300 public centers 1330molec. bio databases Nucleic Acids Research (96 in Jan 2001) 2004: 36 TB 2012: 2,300 TB Climate model intercomparison project (CMIP) of the IPCC

4. Big science has achieved big successes OSG: 1.4M CPU-hours/day, >90 sites, >3000 users, >260 pubs in 2010 LIGO: 1 PB data in last science run, distributed worldwide Robust production solutions Substantial teams and expense Sustained, multi-year effort Application-specific solutions, built on common technology ESG: 1.2 PB climate data delivered to 23,000 users; 600+ pubs All build on NSF OCI (& DOE)-supported Globus Toolkit software

5. But small science is struggling More data, more complex data Ad-hoc solutions Inadequate software, hardware Data plan mandates

6. Medium-scale science struggles too! Blanco 4m on Cerro Tololo Image credit: Roger Smith/NOAO/AURA/NSF • Dark Energy Survey receives 100,000 files each night in Illinois • They transmit files to Texas for analysis … then move results back to Illinois • Process must be reliable, routine, and efficient • The cyberinfrastructure team is not large

7. The challenge of staying competitive "Well, in our country," said Alice … "you'd generally get to somewhere else — if you run very fast for a long time, as we've been doing.” "A slow sort of country!" said the Queen. "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!"

8. Current approaches are unsustainable • Small laboratories • PI, postdoc, technician, grad students • Estimate 5,000 across US university community • Average ill-spent/unmet need of 0.5 FTE/lab? • Medium-scale projects • Multiple PIs, a few software engineers • Estimate 500 across US university community • Average ill-spent/unmet need of 3 FTE/project? • Total 4000 FTE: at ~$100K/FTE => $400M/yr Plus computers, storage, opportunity costs, …

9. And don’t forget administrative costs 42%of the time spent by an average PI on a federally funded research project was reported to be expended on administrative tasks related to that project rather than on research — Federal Demonstration Partnership faculty burden survey, 2007

10. You can run a company from a coffee shop

11. Because businesses outsource their IT Web presence Email (hosted Exchange) Calendar Telephony (hosted VOIP) Human resources and payroll Accounting Customer relationship mgmt Software as a Service (SaaS)

12. And often their large-scale computing too Web presence Email (hosted Exchange) Calendar Telephony (hosted VOIP) Human resources and payroll Accounting Customer relationship mgmt Data analytics Content distribution Software as a Service (SaaS) Infrastructure as a Service(IaaS)

13. Let’s rethink how we provide research IT Accelerate discovery and innovation worldwide by providing research IT as a service Leverage software-as-a-service to • provide millions of researchers with unprecedented access to powerful tools; • enable a massive shortening of cycle times intime-consuming research processes; and • reduce research IT costs dramatically via economies of scale

14. Time-consuming tasks in science • Run experiments • Collect data • Manage data • Move data • Acquire computers • Analyze data • Run simulations • Compare experiment with simulation • Search the literature • Communicate with colleagues • Publish papers • Find, configure, install relevant software • Find, access, analyze relevant data • Order supplies • Write proposals • Write reports • …

15. Time-consuming tasks in science • Run experiments • Collect data • Manage data • Move data • Acquire computers • Analyze data • Run simulations • Compare experiment with simulation • Search the literature • Communicate with colleagues • Publish papers • Find, configure, install relevant software • Find, access, analyze relevant data • Order supplies • Write proposals • Write reports • …

16. Data movement can be surprisingly difficult B A

17. Data movement can be surprisingly difficult Discover endpoints, determine available protocols, negotiate firewalls, configure software, manage space, determine required credentials, configure protocols, detect and respond to failures, determine expected performance, determine actual performance, identify diagnose and correct network misconfigurations, integrate with file systems, … It took 2 weeks and much help from many people to move 10 TB between California and Tennessee. (2007 BES report) B A

18. Globus Online’sSaaS/Web 2.0 architecture Command line interface lsalcf#dtn:/ scpalcf#dtn:/myfile \ nersc#dtn:/myfile HTTP REST interface POST https://transfer.api.globusonline.org/ v0.10/transfer <transfer-doc> Web interface OpenID OAuth Shibboleth (Operate) Fire-and-forget data movement Automatic fault recovery High performance No client software install Across multiple security domains (Hosted on) GridFTP servers FTP servers Other protocols: HTTP, WebDAV, SRM, … Globus Connect on local computers

19. Example application: UC sequencing facility Mac using Globus Connect Delivery of data to customer iBi File Server Mount drive iBi general-purpose compute cluster Sequencing-specific compute cluster Sequencing instrument

20. Statistics and user feedback • Launched November 2010 >1700 users registered >500 TB user data moved >30 million user files moved >150 endpoints registered • Widely used on TeraGrid/XSEDE; other centers & facilities; internationally • >20x faster than SCP • Faster than hand-tuned “Last time I needed to fetch 100,000 files from NERSC, a graduate student babysat the process for a month.” “I expected to spend four weeks writing code to manage my data transfers; with Globus Online, I was up and running in five minutes.” “Transferred 28 MB in 20 minutes instead of 61 hours. Makes these global climate simulations manageable.”

21. Moving 586 Terabytes in two weeks

22. Monitoring provides deep visibility

23. 20 Terabytes in less than one day Terabyte 20 Gigabyes in more than two days Gigabyte Megabyte Kilobyte

24. Common research data management steps • Dark Energy Survey • Galaxy genomics • LIGO observatory • SBGrid structural biology consortium • NCAR climate data applications • Land use change; economics

25. We have choices of where to compute • Campus systems • First target for many researchers • XSEDE supercomputers • 220,000 cores, peer-reviewed awards • Optimized for scientific computing • Open Science Grid • 60,000 cores; high throughput • Commercial cloud providers • Instant access for small tasks • Expensive for big projects Users insist that they need everything connected

26. Towards “research IT as a service”

27. Research data management as a service • GO-Store • Access to campus, cloud, XSEDE storage • GO-Catalog • On-demand metadata catalogs • GO-Compute • Access to computers • GO-Galaxy • Share, create, run workflows Today Prototype • GO-User • Credentials and other profile information • GO-Transfer • Data movement • GO-Team • Group membership • GO-Collaborate • Connect to collaborative tools: Jira, Confluence, … Fall

28. SaaS services in action: The XSEDE vision XUAS

29. Data analysis as a service: Early steps Securely and reliably: • Assemble code • Find computers • Deploy code • Run program • Access data • Store data • Record workflow • Reuse workflow [7, 8] [1, 2] We have built such systems for biological, environmental,and economics researchers VM image App code Workflow Galaxy Condor [3, 4] [5, 6] Data store

30. SaaS economics: A quick tutorial • Lower per-user cost (x10?) via aggregation onto common infrastructure • $400M/yr $40M/yr? • Initial “cost trough” due to fixed costs • Per-user revenue permits positive return to scale • Further reduce per-user cost over time $ 0 Time X10 reduction in per-user cost: $50K  $5K/yr per lab $300K  $30K/yr per project

31. A national cyberinfrastructure strategy? Small and medium laboratories and projects • To providemore capability formore people at less cost … • Create infrastructure • Robust and universal • Economies of scale • Positive returns to scale • Via the creative use of • Aggregation (“cloud”) • Federation (“grid”) P L L L L L L L L L P P P P L L L L L L L L L L L L L L L L L L aa S Research data management Collaboration, computation Research administration

32. Acknowledgments • Colleagues at UChicago and Argonne • Steve Tuecke, Ravi Madduri, Kyle Chard, Tanu Malik, and others listed at www.globusonline.org/about/goteam/ • Carl Kesselman and other colleagues at other institutions • Participants in the recent ICiS workshop on “Human-Computer Symbiosis: 50 Years On” • NSF OCIand MPS; DOE ASCR; and NIH for support

33. For more information • www.globusonline.org; @globusonline: Twitter • Foster, I. Globus Online: Accelerating and democratizing science through cloud-based services. IEEE Internet Computing(May/June):70-73, 2011. • Allen, B., Bresnahan, J., Childers, L., Foster, I., Kandaswamy, G., Kettimuthu, R., Kordas, J., Link, M., Martin, S., Pickett, K. and Tuecke, S. Globus Online: Radical Simplification of Data Movement via SaaS. Communications of the ACM, 2011.

34. Thank you! foster@uchicago.edu www.globusonline.org @globusonline