1. KanseiGenie: From Geni-fication to Orca-fication ─ Status and Demonst ration Anish Arora, Hongwei Zhang, Rajiv Ramnath, Mukundan Sridharan, Wenjie Zeng, Xi Ju July 20, 2009 Anish Arora, Hongwei Zhang, Rajiv Ramnath, Mukundan Sridharan, Wenjie Zeng, Xi Ju July 20, 2009

2. Kansei Today: Deployed on Multiple WSN Fabrics Dreese Sensor Array Occupancy Elevator Temperature Anchor Nodes PeopleNet Stationary Array

3. KanseiGenie Substrates OSU  XSM  TelosB  Stargates (Embedded linux)  Imote2  Cellphone-PSImote mobile array Wayne State  TelosB  Linux Laptops

4. Kansei Architecture © 2004 - mid2008 Web Server Policy Verification/ Deployer Hardware Array Experiment configuration / Output logs Researcher Web Interface File upload, Execution and Clean up Experiment DB Measurement Manager (Chowkidar) Kansei Scheduler Health Status

5. GENI-fication: KanseiGenie Architecture © Q1-Q2 Web Service Layer KanseiGenie Policy Authority Experiment Manager Hardware Array Ticket honoring/ Slice instantiation/ Management calls Researcher Portal calls RSpec adv/ Slice creation Researcher Portal to be implemented Legend: already implemented File upload, Execution and Clean up Slice Manager O&M Manager Measurement Manager (Chowkidar) KanseiGenie Aggregate of Aggregate Manager

6. 6 Substrate and user service tasks done Refactoring of Kansei web interface into Researcher Portal Genification of (most) Kansei user services as web services e.g. implemented (some of the) experiment interaction interfaces as web services Genification of Kansei scheduler CM/AM aspects of Experiment & Slice Management planes Integration with ORCA

7. KanseiGenie Orca Integration Architecture/Status ORCA Site AuthorityORCA Slice Controller Handlers for slice setup/teardown Resource request Event Handlers Researcher Portal Web Services KanseiGenie Experiment Manager Hardware Arrays KanseiGenie Slice Manager KanseiGenie O&M Manager KanseiGenie Measurement Manager KanseiGenie Aggregate of Aggregate Manager File upload, Execution, Viz Ticket request/grant Slice setup/ tear down ORCA Broker Policy Plugin RSpec Repository to be implemented Legend: already implemented Secure SOAPXML-RPCSOAP

8. 8 Demonstration

9. 9 Objectives We demonstrate  Orca integration  Typical KanseiGenie experimentation  Preliminary version of user-client tools  Data injection (hybrid experimentation)  Visualization

10. 10 A typical KanseiGenie experiment KanseiGenie creates a sliver over a set of nodes  User may specify a specific subset of nodes in a substrate  User specification may span multiple substrates KanseiGenie configures tools/services (to execute on implicitly defined slivers), e.g.  To enable interaction between substrates  To enable data injection and exfiltration KanseiGenie rewrites program or creates sliver for data transformers (future roadmap)

11. 11 Orca Integration Status Orca is deployed in KanseiGenie in distributed way  Broker hosted at Wayne State  Site Authority and Service Manager hosted at OSU on different machines Each substrate is configured as a Resource Pool Users request a single lease for specific subset of nodes on a substrate  Current implementation has only one unit of resource in each pool which uses entire array  Implementing a new broker and site policy plugin

12. 12

13. 13 Pursuer Evader Game Scenario Asset

14. 14 Pursuer Evader Game Scenario Asset

15. 15 Pursuer Evader Game Scenario Asset

16. 16 Pursuer Evader Game Scenario Asset

17. 17 Pursuer Evader Game Scenario Asset

18. 18 Scenario details XSM Array Stargate Array Tracking Application Pursuer Logic/ Mobile Agent Event Injections

19. 19 KanseiGenie Experiments by Outside Researchers KanseiNeteye  UCLA  Michigan State  Northwestern  USC  UT, Dallas  City University of Hong Kong  ICT, Australia  University of Sci. & Tech., China  Michigan State  Southeastern University, China  Wayne State  SUNY, Buffalo Several groups at OSU use Kansei for research & education We are motivated by our own federation scenarios:  Multi-Fabric Sensing (seamless tracking, tripwire)  Seamless Regression Testing  Portable Array and Stationary Array  Fabric via Cloud

20. 20 Questions?