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Making Sense of Models

Making Sense of Models. Research and teaching experience Yan Liu Presentation over skype September 19, 2008. Outline. About Me Research Experience Research Vision Teaching Experience Position Expectation. About me. PhD, 2001-2004. University of Sydney

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Making Sense of Models

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  1. Making Sense of Models Research and teaching experience Yan Liu Presentation over skype September 19, 2008

  2. Outline • About Me • Research Experience • Research Vision • Teaching Experience • Position Expectation

  3. About me • PhD, 2001-2004. University of Sydney • Intl. postgraduate research scholarship, Department of Education, Australian Government • Supervisors: Prof. Alan Fekete and Prof. Ian Gordon • Thesis: • A framework of performance prediction of component-based applications • Reviewers: Dr. Len Bass, Prof. John Grundy and Dr. Piyush Maheshwari • Researcher @ NICTA, March 2004 – June 2007 • Lecturer @ School of Computer Science and Engineering (CSE), UNSW, March 2004 – June 2007 • Senior researcher @ NICTA, July 2007 – present • Conjoint senior lecturer @ CSE UNSW, July 2007 – present

  4. Ideas + Models = Applications http://www.langorigami.com/ and http://design.origami.free.fr/Diagrams/cp.htm

  5. PhD Thesis : Performance Prediction Method Applicationdesign model Performance model Architecture model (calibrating) Performance profile (benchmarking) Performance model (populating) Performance Prediction

  6. Research outcomes • publication in IEEE Transactions on Software Engineering, Journal of systems and software, and 3 intl. conferences

  7. How did I march?

  8. Research Experience

  9. From models to applications statistics soft arch. Models queuing theory Mission critical system middle ware stochastic process web tech. System integration and SOAs Internet/Web applications

  10. Making Your System Good Research mission Devising analysis models, architectures and frameworks to improve the performance and dependability of large distributed software systems.

  11. Software architecture evaluation statistics soft arch. Middleware Architecture Evaluation MethodS Models queuing theory middle ware Defence applications How to evaluate the COTS software framework acquired? stochastic process web tech.

  12. Application in mission critical systems

  13. Research outcomes • Two projects funded by Defence Science and Technology Organization (DSTO), Department of Defence, Australia, in 2007 and 2008. • Research reports published by DSTO • Full papers published at QoSA conference • Research collaboration with Dr. Len Bass, SEI/CMU • A TSE submission in writing • A new project with DSTO is under discussion

  14. Performance assessment of SOAs statistics soft arch. egovernment Performance Assessment for Service Architecture (ePASA) Integrated SOAs Models queuing theory middle ware stochastic process web tech. Can the system scale up to handle peak load at the deadline?

  15. Component (QNM equivalent server) Scenarios (i.e. 5 classes of workload) Workload mix Container (software hosting the computing) Host (physical deployment) Service demand (e.g. CPU, Disk, network demand) Application in SOAs

  16. Research outcomes • Corner stone project for a new research group setup at NICTA Canberra Lab • Public breakfast seminar with 30+ attendees from IT companies and government agencies • Media coverage • Nominated for NICTA research impact awards • Full paper at published at CBSE, Boston, 2007

  17. Adaptive middleware statistics soft arch. Models queuing theory middle ware Adaptive Middleware Platform (AMP) stochastic process web tech. Can models drive the adaptation? And how?

  18. Application in self-managing applications (1/3) Multi-classtoken bucket algorithm Queued Petri Net model

  19. Application in self-managing applications (2/3)

  20. Application in self-managing applications (3/3)

  21. Research outcomes • Research fund for 2 years • A software prototype ready for trial (developing license with NICTA legal department) • Techniques filed for invention disclosure • Published conference and journal papers (journals: SPE, JSS; conferences: QoSA, ICWS, ICSOC; workshop papers: SDSOA, SEAMS)

  22. Microkernel-based embedded systems statistics soft arch. http://www.ok-labs.com/ Models queuing theory middle ware Can low level OS libraries be modules and components? stochastic process web tech.

  23. 6 1 5 3 4 2 Application in embedded OS

  24. Application in embedded OS Verifying CAmkES components and connectors uses provides IguanaRPC Client Server “add” “add” CAmkES send interface receive interface receive interface send interface PnP ports

  25. Research outcomes • A software for open source (getting internal paper work) • Published conference and journal papers (CBSE, QoSA, ASWEC, and JSS) • Research collaboration with Prof. Lori Clarke at University of Massachusetts Amherst

  26. Research Vision

  27. statistics soft arch. Resource allocation, valuation in Ultra Large Scale Systems (ULSS) Models queuing theory middle ware stochastic process web tech. Market Models

  28. Applying market-based approach to ULSS

  29. Teaching Experience

  30. Course teaching • Lecturer of Architecture of Software Systems–COMP 9117, July 2006 • School of Computer Science and Engineering, University of New SouthWales • 4th year software engineering degree undergrads, and postgrads • Design pattern, component-based development, services, software architecture and framework, AOP, model driven development

  31. Student supervision • Spin-off student projects from research activities • Introduce ‘taste-of-research’ project to 4th year undergraduate students • Students always give you a surprise if you really work with them as a team • Totally 39 students (2004 – now)

  32. Example student projects Adaptive Middleware for Mobile Application Mashup for property valuation Performance analysis of enterprise service bus OSGI components for wireless sensors

  33. Supervision statistics

  34. Position Expectation

  35. Skills vs expectation • Research leadership • Steer research direction • Apply research funds • Manage budget • Manage R&D activities • Teaching experience • Lecturer and course admin • Student supervision • Professional skills • Programming Support for research, funding application and Industry collaboration Supervision of postgrad students

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