1. Instruments and Sensors as Grid Services Donald F. (Rick) McMullen 1 Kenneth Chiu 2 John C. Huffman 1 Kia Huffman 1 Randall Bramley 1 1 Indiana University 2 SUNY Binghamton

2. 2 Motivations Instruments and sensors are not well integrated into grids Data is acquired, processed and stored before it hits the grid. Need methodology for interacting with instruments and sensors in real time from grid applications Some abstraction of sensor and instrument functionality is needed to make grid applications that use them more robust and flexible.

3. 3 Goals Integrate instruments and sensors (e.g. real-time data sources) into a Grid computing environment with Grid services interfaces. Abstract instrument capabilities and functions to reduce data acquisition and analysis applications dependence on specialized knowledge about particular instruments. Move production of metadata as close to instruments as possible and facilitate the automatic production of metadata. Develop a standard, reusable methodology for grid enabling instruments. Collaborate with scientists in academia and industry in a broad range of disciplines who either develop instruments or whose work depends on the details of using them.

4. 4 Increasing number of sensors Increasing bandwidth per sensor Increasing real-time application X-Ray Crystallography Electron Microscope Radio Telescope Ground motion sensor array Traffic sensors Wireless mote sensor network Simple 2-D analysis of instrument taxonomy At lease five dimensions identified in more detailed analysis Project must address enough points (classes) to assure breadth of applicability

5. 5 Initial Applications High brilliance X-ray crystallography –Large instrument application –Deeply integrated into bio and medical discovery research methodology –Mature analysis software and large user community Robotic telescopes –Small numbers of sensors: CCD, environmental; some control aspects: filters, aiming, dome. –Global coordination needed for scheduling –Aggregation of disparate sensors into a composite instrument Small sensors –Minimal memory and CPU –Wireless connectivity –Developing parallel project to use ad hoc/swarm networks for data collection for real-time simulation and prediction –Updating of data flows in response to sensor/network reconfiguration.

6. 6 CIMA implementation targets MICA Mote wireless sensor/controller board PC104 industrial controller board Synchrotron beamline (APS/ALS) Large scientific instruments Embedded sensors and controllers very large systems, few elements very small systems, many elements

7. 7 MMSF Automated Telescope Typical remote access automated observatory System has 33 distinct sensors, 12 controllers –Open/close of roof based on a Polaris transparency monitor and rain detector – simple grid of wires detecting rain drops –Telescope direction and dome control –Filter selection and telescope focus –Liquid nitrogen fills of the CCD dewar jars ….

8. 8 MMSF Observatory Features Instruments producing data without units –Temperature, humidity cutoffs determined empirically as resistances, not degrees or % Hierarchical and co-located instruments –Single platform holds three instruments, so orienting one changes orientation of all Updates to equipment occurs frequently Data transferred via 28k modem line – middleware needs to work locally, directly between instruments and sensors

9. 9 Observatory Data Architecture Control Data –object control –instrument control Object Data (i.e., object of scientific interest) –Full spectrum from raw unitless data to derived data artifacts Instrument package –system package data (multiple attributes output) –system sensor data (single attribute output) –nonsystem sensor data (weather data from NOAA) –calibration data –access protocols

10. 10 X-Ray Crystallography Instrument Services Portal Instrument Manager Data Archive WAN LAN Non-grid service Grid service Persistent Non-persistent Proxy Box

11. 11 LTER Automatic updating of flows Provenance SOAP/ARTS (Antelope Real-Time System)

12. 12 Further applications currently being explored Robotic telescopes - Bradford robotic telescope, Oxenhope observatory, Faulkes telescope project Electron Microscopy -U. Queensland EM group – regional scale, multiple instruments - KISTI 2nd largest EM (after Osaka) Industrial monitoring and control, e.g. Train axles - Ore trains – Km long, derailment is very expensive to fix - Temperature sensors on axles monitor bearing status, anticipate wheel failure Environmental monitoring - Water use - contaminants

13. 13 Architecture

14. 14 Common Instrument Middleware Architecture (CIMA) Elements Schema for instrument functionality (and ontology for schema attributes); Data model for representing instrument metrics and calibration; A small, high performance, embeddable Web Services stack, initially in Java, including Proteus support for multi-protocol, multimodal transport; Service implementation for accessing the instruments functionality and metrics via the Proteus-mediated interface; –Ability to dynamically insert new protocols into running instances OGSA and WS-RF compliant functions to register with a location service, authenticate users, provide access control to instrument controls and data, send and receive events, and co-schedule the instrument into a Grid computing and storage context.

15. 15 Overview

16. 16

17. 17 Example CIMA minimal knowledge bootstrap procedure Application CIMA instrument send description RDF description Proteus/SOAP calls Application parses description for ports to read for calibration and voltage read calibration port calibration read thermocouple port thermocouple voltage Service Implementation (SI) returns description of itself and instrument SI returns stored calibration SI calls controller function to read and return voltage Application reads thermocouple voltage then computes and displays a temperature Globusinit, user authentication, and instrument lookup

18. 18 Instrument Services Instrument –get –set Sensor –get –set ChannelSource –get –set –register ChannelSink –receive –get –set Sensor Channel Source Sensor Channel Source Channel Sink

19. 19 Parcels Wish to unify our data models, etc. Toolkit must be application-independent as much as possible. Attributes –Type (string) –Globally unique ID (string) –Encoding CDATA, Binary, ASCII, Base64 –Location Inline, URL, Other Parcel Sets –Special data used for connectivity information.

20. 20 Technologies

21. 21 Technologies Web services –XML, SOAP, WSDL, binary XML Grid services –OGSA/OGSI, WS-RF, DAIS Axis C++ gSOAP Proteus (SC 2002) XBS (HPC 2004) Schema-specific parsing

22. 22 Proteus Motivation Web Services for scientific computing –SOAP performs well as a lingua franca But suffers from performance problems for scientific data –Solution: establish initial communication with SOAP, and then switch to a faster protocol. Grid intermediaries

23. 23 Proteus Overview Provides multiprotocol RMI system to applications –Can wrap existing protocol implementations with dynamic invocation Facilitates use of SOAP as common language –Switch to faster protocols if supported by both sides. Mediates between protocol providers and applications –Applications use Proteus client API –Providers use Proteus provider API Allows a new provider to be added (at run-time) without changing application Generic serialization/deserialization allows marshalling code to be reused for multiple protocols

24. 24 Multiprotocol Network Proteus Client Provider A Provider B Proteus Client Provider A Provider B Protocol A Protocol B Process 1Process 2

25. 25 Proteus Protocol A Provider Application Protocol B Provider Proteus APIs Client API Provider API

26. 26 Schema-Specific Parsing XML processing stages (conceptual) –Well-formedness Lexical and syntactic, defined by core XML specification. –Validity Conformance to a schema, mainly structural –Application

27. 27 Merging Stages Application Validation Well- formedness Application Validation Well- formedness Application Validation Well- formedness User written Merged Fully articulatedImplicitly validatedSchema-specific parsing

28. 28 Compiler-Based Approach Front-end parses schema into intermediate representation. Back-end generates code from intermediate representation. Intermediate representation is a generalized automata. XML Schema IR RELAX NG C (fast) C (low-pow) Java

29. 29 Summary Create standards for accessing broad spectrum instruments and sensors. Incompatible components should still have some base level of interoperability.