So Long Computer Overlords

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The title of this talk is a crass attempt to be catchy and topical, by referring to the recent victory of Watson in Jeopardy.

My point (perhaps confusingly) is not that new computer capabilities are a bad thing. On the contrary, these capabilities represent a tremendous opportunity for science. The challenge that I speak to is how we leverage these capabilities without computers and computation overwhelming the research community in terms of both human and financial resources. The solution, I suggest, is to get computation out of the lab—to outsource it to third party providers.

Abstract follows:

We have made much progress over the past decade toward effective distributed cyberinfrastructure. In big-science fields such as high energy physics, astronomy, and climate, thousands benefit daily from tools that enable the distributed management and analysis of vast quantities of data. But we now face a far greater challenge. Exploding data volumes and new research methodologies mean that many more--ultimately most?--researchers will soon require similar capabilities. How can we possible supply information technology (IT) at this scale, given constrained budgets? Must every lab become filled with computers, and every researcher an IT specialist?

I propose that the answer is to take a leaf from industry, which is slashing both the costs and complexity of consumer and business IT by moving it out of homes and offices to so-called cloud providers. I suggest that by similarly moving research IT out of the lab, we can realize comparable economies of scale and reductions in complexity, empowering investigators with new capabilities and freeing them to focus on their research.

I describe work we are doing to realize this approach, focusing initially on research data lifecycle management. I present promising results obtained to date, and suggest a path towards large-scale delivery of these capabilities. I also suggest that these developments are part of a larger "revolution in scientific affairs," as profound in its implications as the much-discussed "revolution in military affairs" resulting from more capable, low-cost IT. I conclude with some thoughts on how researchers, educators, and institutions may want to prepare for this revolution.

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  • I wanted a catchy title, so I chose one that referred to the recent victory of Watson overBrad Rutter and Ken Jenningsin Jeopardy.
  • But my point (perhaps confusingly) is not that new computer capabilities are a bad thing. On the contrary, these capabilities represent a tremendous opportunity for science.The challenge that I want to speak to is how we leverage these capabilities without computers and computation overwhelming the research community in terms of both human and financial resources.The solution, I will suggest, is to get computation out of the lab—to outsource it to third party providers. I will explain how this task can be achieved.
  • The need to deal with and benefit from large quantities of data is not a new concept: it has been noted in many policy reports, particularly in the US and UK, over the past several years.Series of policy reports, particularly in the US and UK, about the new models of science, and investments to be madeA sampling of key reports, in chron order:Atkins report, 2003 – laid out the vision of cyberinfrastructure – which was also used as a roadmap by the UK for their eScience programNSB Long lived data report, 2005 – defined data, data scientists, and laid out capture and curation issues2020 Science – 2006, outlining the data and computational nature of scienceNSF Vision doc, 2007, consolidated the Atkins report, LL data report, others, to layout a programmatic plan. Datanet, Cyberenabled discovery and innovation came from this planRecent ACI report on data and viz.Harnessing the power – NITRD, 2009, for federal agenciesRCUK eScience reviewBlue ribbon panel on economics of curation
  • But now the data deluge is now upon us. I use a few examples to highlight developments:-- Genome sequencing machines are doubling in output every nine months. This leaves the rather stately 18 month Moore’s Law doubling of computer performance in the shade.-- Astronomy, which only entered the digital era around 2000, projects 100,000 TB data from LSST by the end of the decade. [2MASS completed 2001; -- Simulation -- And not just volume, but also complexityTrends: Scale, complexity, distributed generation, …--------Source for genomic data: http://www.sciencemag.org/content/331/6018/728.short (“Output from next-generation sequencing (NGS) has grown from 10 Mb per day to 40 Gb per day on a single sequencer, and there are now 10 to 20 major sequencing labs worldwide that have each deployed more than 10 sequencers “)Source for mol bio dbs: http://nar.oxfordjournals.org/content/39/suppl_1/D1.full.pdf+htmlSource for climate change image: http://serc.carleton.edu/details/images/17685.html
  • Not just small labs—medium science too.E.g., Dark Energy Survey.
  • For many researchers, projects, and institutions, large data volumes are not an opportunity but a fundamental challenge to their competitiveness as researchers. How can they keep up?
  • 200 universities * 250 faculty per university = 5,000Summary:-- Big projects can build sophisticated solutions to IT problems-- Small labs and collaborations have problems with both--They need solutions, not toolkits—ideally outsourced solutions
  • Need date
  • Of course, people also make effective use of IaaS, but only for more specialized tasks
  • More specifically, the opportunity is to apply a very modern technology—software as a service, or SaaS—to address a very modern problem, namely the enormous challenges inherent in translating revolutionary 21st century technologies into scientific advances. Midway’s SaaS approach will address these challenges, and both make powerful tools far more widely available, and reduce the cycle time associated with research and discovery.Achieve economies of scaleReduce cost per researcher dramaticallyAchieve positive returns to scaleMost academic solutions do NOT have PRTSMost industrial solutions DO have PRTS
  • So let’s look at that list again.I and my colleagues started an effort a little while ago aimed at applying SaaS to one of these tasks …
  • Example: small lab generates data at Texas Advanced Computing Center or the Advanced Photon Source. Needs to move it back to their lab.Or: Needs to move data from experimental facility (e.g., sequencing center or Dark Energy Survey) to computing facility for analysis.
  • Data movement is conceptually simple, but can be surprisingly difficult
  • Why? Discover endpoints, determine available protocols, negotiate firewalls, configure software, manage space, determine required credentials, configure protocols, detect and respond to failures, identify diagnose and correct network misconfigurations,…
  • •Reliable file transfer. –Easy “fire and forget” file transfers –Automatic fault recovery –High performance –Across multiple security domains•No IT required. –No client software installation –New features automatically available –Consolidated support and troubleshooting –Works with existing GridFTP servers –Globus Connect solves “last mile problem”
  • I’ll talk about integration with the Galaxy workflow system later …
  • Reduce costs.Improve performance.Enable new science.
  • What else do we need?
  • Add university logos?
  • Slide 33: Is the task of creating reusable workflows part of these 6 steps? Is publication and discovery of workflows/derived data products part of this as well? Is reproducible research part of it as well?
  • Researchers vote with their dollars
  • Before-- Lots of little labs-- Big science-- XSEDE After:lots of empowered SMLs, entrepreneurship in science, reproducible/reusable research etc
  • So Long Computer Overlords

    1. 1. So long, computer overlordsHow Cloud (and Grid) can liberate research IT – and transform discoveryIan Foster<br />
    2. 2.
    3. 3.
    4. 4. The data deluge<br />MACHO et al.: 1 TB<br />Palomar: 3 TB<br />2MASS: 10 TB<br />GALEX: 30 TB<br />Sloan: 40 TB<br />Pan-STARRS: 40,000 TB<br />100,000 TB<br />Genomic sequencing output x2 every 9 month<br />>300 public centers<br />1330molec. bio databases Nucleic Acids Research (96 in Jan 2001)<br />2004: 36 TB<br />2012: 2,300 TB<br />Climate model intercomparison<br />project (CMIP) of the IPCC<br />
    5. 5. Big science has achieved big successes<br />OSG: 1.4M CPU-hours/day, >90 sites, >3000 users, >260 pubs in 2010<br />LIGO: 1 PB data in last science run, distributed worldwide<br />Robust production solutions<br />Substantial teams and expense<br />Sustained, multi-year effort<br />Application-specific solutions, built on common technology<br />ESG: 1.2 PB climate data<br />delivered to 23,000 users; 600+ pubs<br />All build on NSF OCI (& DOE)-supported Globus Toolkit software<br />
    6. 6. But small science is struggling<br />More data, more complex data<br />Ad-hoc solutions<br />Inadequate software, hardware<br />Data plan mandates<br />
    7. 7. Medium-scale science struggles too!<br />Blanco 4m on Cerro Tololo<br />Image credit: Roger Smith/NOAO/AURA/NSF<br />Dark Energy Survey receives 100,000 files each night in Illinois<br />They transmit files to Texas for analysis … then move results back to Illinois<br />Process must be reliable, routine, and efficient<br />The cyberinfrastructure team is not large<br />
    8. 8. The challenge of staying competitive <br /> "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.”<br /> "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!"<br />
    9. 9. Current approaches are unsustainable<br />Small laboratories<br />PI, postdoc, technician, grad students<br />Estimate 5,000 across US university community<br />Average ill-spent/unmet need of 0.5 FTE/lab?<br />Medium-scale projects<br />Multiple PIs, a few software engineers<br />Estimate 500 across US university community<br />Average ill-spent/unmet need of 3 FTE/project?<br />Total 4000 FTE: at ~$100K/FTE => $400M/yr<br /> Plus computers, storage, opportunity costs, …<br />
    10. 10. And don’t forget administrative costs<br />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<br /> — Federal Demonstration Partnership faculty burden survey, 2007<br />
    11. 11. You can run a company from a coffee shop<br />
    12. 12. Because businesses outsource their IT<br />Web presence<br />Email (hosted Exchange)<br />Calendar<br /> Telephony (hosted VOIP)<br /> Human resources and payroll<br /> Accounting<br /> Customer relationship mgmt<br />Software as a Service<br />(SaaS)<br />
    13. 13. And often their large-scale computing too<br />Web presence<br />Email (hosted Exchange)<br />Calendar<br /> Telephony (hosted VOIP)<br /> Human resources and payroll<br /> Accounting<br /> Customer relationship mgmt<br /> Data analytics<br /> Content distribution<br />Software as a Service<br />(SaaS)<br />Infrastructure as a Service(IaaS)<br />
    14. 14. Let’s rethink how we provide research IT<br />Accelerate discovery and innovation worldwide by providing research IT as a service<br />Leverage software-as-a-service to<br />provide millions of researchers with unprecedented access to powerful tools; <br />enable a massive shortening of cycle times intime-consuming research processes; and<br />reduce research IT costs dramatically via economies of scale<br />so long, <br />computer overlords <br />
    15. 15. Time-consuming tasks in science<br />Run experiments<br />Collect data<br />Manage data<br />Move data<br />Acquire computers<br />Analyze data<br />Run simulations<br />Compare experiment with simulation<br />Search the literature<br /><ul><li>Communicate with colleagues
    16. 16. Publish papers
    17. 17. Find, configure, install relevant software
    18. 18. Find, access, analyze relevant data
    19. 19. Order supplies
    20. 20. Write proposals
    21. 21. Write reports
    22. 22. …</li></li></ul><li>Time-consuming tasks in science<br />Run experiments<br />Collect data<br />Manage data<br />Move data<br />Acquire computers<br />Analyze data<br />Run simulations<br />Compare experiment with simulation<br />Search the literature<br /><ul><li>Communicate with colleagues
    23. 23. Publish papers
    24. 24. Find, configure, install relevant software
    25. 25. Find, access, analyze relevant data
    26. 26. Order supplies
    27. 27. Write proposals
    28. 28. Write reports
    29. 29. …</li></li></ul><li>Data movement can be surprisingly difficult<br />B<br />A<br />
    30. 30. Data movement can be surprisingly difficult<br /> 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, …<br />It took 2 weeks and much help from many people to move 10 TB between California and Tennessee.<br />(2007 BES report)<br />B<br />A<br />
    31. 31. Globus Online’sSaaS/Web 2.0 architecture<br />Command line interface<br />lsalcf#dtn:/<br />scpalcf#dtn:/myfile <br />nersc#dtn:/myfile<br />HTTP REST interface<br />POST https://transfer.api.globusonline.org/ v0.10/transfer <transfer-doc><br />Web interface<br />(Operate) <br />Fire-and-forget data movement<br />Automatic fault recovery<br />High performance<br />No client software install<br />Across multiple security domains<br />(Hosted on) <br />GridFTP servers<br />FTP servers<br />Other protocols:<br />HTTP, WebDAV, SRM, …<br />Globus Connect<br />on local computers<br />
    32. 32. Example application: UC sequencing facility<br />Mac using Globus Connect<br />Delivery of data to customer<br />iBi File Server<br />Mount drive<br />iBi general-purpose compute cluster<br />Sequencing-specific compute cluster<br />Sequencing instrument<br />
    33. 33. Statistics and user feedback<br />Launched November 2010<br />>1400 users registered<br />>350 TB user data moved<br />>28 million user files moved<br />>140 endpoints registered<br />Widely used on TeraGrid/XSEDE; other centers & facilities; internationally<br />>20x faster than SCP<br />Faster than hand-tuned <br />“Last time I needed to fetch 100,000 files from NERSC, a graduate student babysat the process for a month.”<br />“I expected to spend four weeks writing code to manage my data transfers; with Globus Online, I was up and running in five minutes.”<br />“Globus Online’s speed has us planning experiments that we would never have considered previously.”<br />
    34. 34. Moving 586 Terabytes in two weeks<br />
    35. 35. Monitoring provides deep visibility<br />
    36. 36. 20 Terabytes in less than one day<br />Terabyte <br />20 Gigabyes in more than two days<br />Gigabyte <br />Megabyte <br />Kilobyte <br />
    37. 37. Common research data management steps<br />Dark Energy Survey<br />Galaxy genomics<br />LIGO observatory<br />SBGrid structural biology consortium<br />NCAR climate data applications<br />Land use change; economics<br />
    38. 38. We have choices of where to compute<br />Campus systems<br />First target for many researchers<br />XSEDE supercomputers<br />220,000 cores, peer-reviewed awards<br />Optimized for scientific computing<br />Open Science Grid<br />60,000 cores; high throughput<br />Commercial cloud providers<br />Instant access for small tasks<br />Expensive for big projects<br />Users insist that they need everything connected<br />
    39. 39. Towards “research IT as a service” <br />
    40. 40. Research data management as a service<br />GO-User<br />Credentials and other profile information<br />GO-Transfer<br />Data movement<br />GO-Team<br />Group membership<br />GO-Collaborate<br />Connect to collaborative tools: Jira, Confluence, …<br />GO-Store<br />Access to campus, cloud, XSEDE storage<br />GO-Catalog<br />On-demand metadata catalogs<br />GO-Compute<br />Access to computers<br />GO-Galaxy<br />Share, create, run workflows<br />Today<br />Prototype<br />Fall<br />
    41. 41. SaaS services in action: The XSEDE vision<br />XUAS<br />
    42. 42. Data analysis as a service: Early steps<br />Securely and reliably:<br />Assemble code<br />Find computers<br />Deploy code<br />Run program<br />Access data<br />Store data<br />Record workflow<br />Reuse workflow<br />[7, 8]<br />[1, 2]<br />We have built such systems for biological, environmental,and economics researchers<br />VM image<br />App code<br />Workflow<br />Galaxy<br />Condor<br />[3, 4]<br />[5, 6]<br />Data store<br />
    43. 43. SaaS economics: A quick tutorial<br />Lower per-user cost (x10?) via aggregation onto common infrastructure<br />$400M/yr $40M/yr?<br />Initial “cost trough” due to fixed costs<br />Per-user revenue permits positive return to scale <br />Further reduce per-user cost over time<br />$<br />0<br />Time<br />X10 reduction in per-user cost:<br /> $50K  $5K/yr per lab<br /> $300K  $30K/yr per project<br />
    44. 44. A national cyberinfrastructure strategy?<br />To providemore capability formore people at less cost …<br />Create infrastructure <br />Robust and universal<br />Economies of scale<br />Positive returns to scale<br />Via the creative use of<br />Aggregation (“cloud”)<br />Federation (“grid”)<br />Small and medium laboratories and projects<br />P<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />P<br />P<br />P<br />P<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />L<br />aa<br />S<br />Research data management Collaboration, computation<br />Research administration<br />
    45. 45. Acknowledgments<br />Colleagues at UChicago and Argonne<br />Steve Tuecke, Ravi Madduri, Kyle Chard, Tanu Malik, Michael Russell, Paul Dave, Stuart Martin, Dan Katz, and many others<br />Colleagues at other institutions<br />Carl Kesselman, MironLivny, John Towns, and others<br />NSF OCI, MPS, and SBE; DOE ASCR; and NIH for support<br />
    46. 46. For more information<br />Foster, I. Globus Online: Accelerating and democratizing science through cloud-based services. IEEE Internet Computing(May/June):70-73, 2011.<br />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.<br />
    47. 47. Thank you!<br />foster@uchicago.edu<br />

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