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ORION CI Conceptual Architecture Team Progress Report

ORION CI Conceptual Architecture Team Progress Report. Agenda Context Presentation (Options) Discussion. Context. ORION Context. Expanding Role of Environmental Observing. From individual expedition to collaborative observation.

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ORION CI Conceptual Architecture Team Progress Report

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  1. ORION CI Conceptual Architecture TeamProgress Report Agenda Context Presentation (Options) Discussion

  2. Context

  3. ORION Context

  4. Expanding Role of Environmental Observing • From individual expedition to collaborative observation. • Long-term persistent presence, continuous 24/7 time series • Increasing numbers and diversity of sensors • Multiple, complex data streams • Many more types of users and applications • Ability to control instruments, observing systems, and large-scale experiments. • Ubiquitous data requiring greater differentiated expertise. • From kilobits to terabytes in a matter of hours. • Collaborative discovery by groups of researchers with different vocabularies and expertise looking at complex data streams. • A need for cooperating networks of data/processing/analysis systems that are different than in the past.

  5. Team Members Matthew Arrott (UCSD, Chair) Alan Chave (WHOI) John Graybeal (MBARI) Eric Guillemot (NEPTUNE Canada) Ingolf Krüger (UCSD) Benoît Pirenne (NEPTUNE Canada)

  6. Team Mandate • Develop a set of information system capabilities. • Develop conceptual level design options to support the Ocean Observatories Initiative (OOI) systems operations and RFP development. • Develop a CI Work Breakdown Structure (WBS). • Create resource estimates for each line in the WBS to prepare a Rough Order of Magnitude (ROM) cost and timeline for the project.

  7. What do you want us to talk about?

  8. Presentation Options • How will using ORION be different from the way science is done today? • What will CI do for us as individuals and as a community? • How has CI been implemented in other environments? • What is the current architectural concept? • How can we be sure this is a viable, implementable infrastructure? • How did you go about putting this together?

  9. How will using ORION be different from the way science is done today?

  10. Getting A Book: The Way It Used To Be A Parable I need to special order a book. What book do you want? It’s by Rachel Carson, Silent Spring. Yes, we can order that. It’s 23.95. When will you have it? Maybe Tuesday, but if it’s not in stock, not for a month. Can you send it to me? No, you’ll need to pick it up. OK, can I pay by phone? Yes, we’ll need your credit card and address. OK, here’s my card info and address… OK, we’ll call you when it’s in.

  11. Getting A Book: The Way It Is Now

  12. Doing Science: The Way It Has Been Hey Francisco, you still running that mooring in Monterey? Yeah, John, what do you need? Is it collecting current data? Yeah, you looking for profiles? Yup, are you doing that with an ADCP? Yes, we’ll probably start profiling soon, too. No, don’t need that. How do I get the data? You want the raw data or QCd? Real-time?. Oh, the QC’d, delayed mode data, definitely, last 3 years worth. That’d be off our dods site, here’s the address.. Documentation come with that? We got some, I’ll have to get one of my techs to send it to you…

  13. Doing Science: The Way It Is and Will Be

  14. Doing Science: Another Option

  15. Or…

  16. Interfacing with ORION Working with the system will be at least as easy as it is now. • Building an instrument? • Instrument interfaces are defined • Libraries and adapters are provided to help you. • Core instruments will be taken care of. • Discovering data? • Lots of options, most of which actually work. • Just “get the data” (as you want it) once you find it. • Not just ORION data, data from lots of places. • Running an observatory? • Access to system status. • Ability to coordinate the entire system as needed.

  17. Return to menu Forward to science scenarios

  18. A Science Scenario • What we have here • An example for illustration • Only one of many applicable science scenarios • The example • “Developing a complex instrument for ORION” • Actually a platform, but we’ll call it an instrument here • Covers most of the lifecycle of the instrument • And furthermore… • Accessing and using data from the instrument

  19. Developing a Complex Instrument for ORION • Platform carrying multiple sensors • Commandable • Not always connected • Multiple non-trivial data sets • Data transport varies • may be stored and copied out of the instrument later • may be streamed in (near) real time

  20. Steps Along The Way

  21. Develop: Create Instrument What does my instrument have to do? What does ORION say my instrument must do? Can I really justify building this? (May call for research.) ORION IO may give me funds (e.g., for core systems)! In any case, I have to get money (may involve research). Finally, I’m building the instrument. ORION just sent me instructions on testing my instrument. I guess I’d better test it. Darn, need to fix that./Yay, it’s ready!

  22. Commission: Test and Validate Instrument Does system know about my instrument? Does my instrument talk to system correctly? Put instrument in the water and hook it up. Make sure the instrument is “behaving.” Make sure my instrument is working correctly. Tell system about my instrument. Record the transaction.

  23. Publish: Announce Availability of Instrument Declare what is the final version of my instrument Describe my instrument (using ORION forms). Make sure this description is valid and complete. Enter my instrument into the approved list. Group this with the other instruments of similar products. Tell everyone this instrument is available.

  24. Discover: Find the Instrument What kind of an instrument am I looking for? What’s the best way to find it? (How & where?) OK, I’ll browse/ search using this interface. Son of a gun, I didn’t know they have a forbitz! Let’s look over here, this list looks relevant. Start the search already. Describe the details I’m looking for. Darn, need to try again./Yay, found it!

  25. Acquire: Get OK to Command Instrument Please let me control the frobnitz (private) instrument. “Can user control this thing?” Do you agree to call the provider now and give credit later? “Sure, if user calls me and credits me.” Never mind, I didn’t want to use that instrument anyway. Of course. “Authorizing access, please wait.” Your instrument access list includes: frobnitz ID#2365 “Instrument documentation is in the mail.” Hi, Pat? Jan here.

  26. Use: Command Instrument This is an instrument I can command. “set sample rate on ‘instrument 23493’ =10 Hz for 3600 secs” Changing sample rate to 10 Hz for 1 hour. I’m getting the data back, I can start my analysis now. I’d better cite that scientist like I agreed to. Thank you for giving me credit on your paper. Do I need to do more? OK, don’t need to command it any more.

  27. Govern: Restrict Instrument Control Define access rights and policies for instrument Is it OK for this user to use the instrument? “Is it, or is it not, OK to use instrument right now? Is it still OK to use the instrument (so much)?

  28. What About the Data? Forward to data access scenarios Return to menu

  29. Publish: Announce Data Available Decide on the final format for data stream(s). Describe data stream (using ORION forms). Make sure this description is valid and complete. Enter my stream into the list of available data. Group this data with the other similar products. Tell everyone this data product is available.

  30. Discover: Find the Data What kind of an data am I looking for? What’s the best way to find it? (How & where?) OK, I’ll browse/ search using this interface. OK, it knows me and is letting me see the data set. Son of a gun, I didn’t know they have carbon data! Let’s look over here, this data set relevant. Describe the data sets I want in detail. Start the search already. Darn, need to try again./Yay, found it!

  31. Acquire: Obtain the Data Please give me access to the frobnitz data stream. Can user have data? Sure, if user agrees to disclaimer Do you accept this data is not quality controlled? Oh, I didn’t want the data if it isn’t Quality Controlled. Yes. “Authorizing access, please wait.” Oh look, the download button’s enabled. Click on link, watch data stream in. (done)

  32. Use: Modify Data, Present Results This is just data. On we go. I’m getting data, my software’s running, I detected upwelling! I write my paper. I’d better cite that scientist like I agreed to. Thank you for giving me credit on your paper. Can I turn this stream off? Yup, don’t need to access it any more.

  33. Return to menu

  34. What will CI do for us as individuals and as a community?

  35. For the individual • Provide workflow and resource management tools to automate experimental design and execution • Provide a secure work environment that automatically mediates conflicts • Generalize and automate query and publish/subscribe processes for retrospective and real-time data

  36. Dynamic Data Driven Assimilation System (DDDAS) use case • Workflow binds diverse fixed and mobile instruments to assimilation model and data repository on shore • Resource conflict mediation is automated

  37. Distributed remote multi-mission laboratories distributed on an RCO • Resource intensive, shared use operations require automated brokering • Quality of service policy constraints are important

  38. For the community • Facilitates collaborative experimentation and communication • Automated integration of ORION policies and external obligations • Ability to archive data with reliable discovery services for future use

  39. Automated tracking and coordination of the state of observatory resources • External resources can be integrated with observatory assets and operations • Science use case becomes the observatory use case

  40. Return to menu

  41. How has CI been implemented in other environments?

  42. CI: Evolved from Grid Computing • Grid Computing is a term for loosely coupled distributed computing across a diverse community of resources, owners • Many Grid Computing efforts and products • Computational Grid (TeraGrid, Open Science Grid) • Decomposition of large problems into many small atomic tasks • Data Grid (Storage Resource Broker, OGSA-DAI) • Federation & cataloguing of distributed data repositories • Service Grid (On-Demand and Location-based service models—IBM, Microsoft) • Generalization of computational and data grids as “Service” patterns • Emergence of Service Oriented Architectures • Autonomic Grid (DMTF, WSDM from HP, IBM, CA, Oracle, Opsware) • Autonomous resource management, load balancing and fault detection isolation and recovery applied to networks of coupled resources • Grid computing contributes to overall cyberinfrastructure

  43. ORION CI Building on Other CI Projects • GriPhyN, Atlas, Ligo, CMS • Data distribution and shared computational grid • NVO (National Virtual Observatory) • Community data model, and shared data repositories and applications • BIRN (Biomedical Information Resource Network) • Federated data repositories of disparate data models into a common meta-catalog • Resolution of disparate data models through mediation • GEON (GEOsciences Network) • Extends data mediation model with ontologies • TeleScience and NEES • Developed real-time control of remote instrumentation and the coupling of remote physical and simulated systems

  44. Environmental Cyberinfrastructures like ORION • What other projects are most like this? • LEAD (Linked Environments for Atmospheric Discovery) • NEON (National Ecological Observatory Network) • What makes them similar? • Significant increase in the number and diversity of instruments and data products • Real-time stream processing and inquiry • Interaction with the sensing environment • Semantic heterogeneity (mixed vocabularies)

  45. Return to menu

  46. What is the current architectural concept?

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