1 making sense of models research and teaching experience yan liu presentation over skype september...

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 client broker account stockitem stockholding stocktx Transaction

Manager BuyStock (visits=7%)

check account credit (ratio=1) get stock price (ratio=1)

update (ratio=1)

start transaction

transaction rollback if there is not enough credit (ratio=1)

commit transaction

insert transaction record (ratio=1)

client broker account stockitem stockholding stocktx Transaction Manager

BuyStock (visits=7%)

check account credit (ratio=1) get stock price (ratio=1)

update (ratio=1)

start transaction

transaction rollback if there is not enough credit (ratio=1)

commit transaction

insert transaction record (ratio=1)

R/W

FindByPK

CS

LC A/P

Y [hr] N [1-hr]

R [p] W [1-p]

Cached

LC A/P

Y [h] N [1-h]

Cached

LD

R or W

SD

W [1-p]R [p]

R/W

FindByPK

CS

LC A/P

Y [hr] N [1-hr]

R [p] W [1-p]

Cached

LC A/P

Y [h] N [1-h]

Cached

LD

R or W

SD

W [1-p]R [p]

0200400600800

100012001400

50 100 200 300 400 500Resp

on

se

tim

e (

R)

in

ms, re

ad

-on

ly in

ten

siv

e CMP Predicted

CMP Measured

RM Predicted

RM Measured

OCC Predicted

OCC Measured0

200400600800

100012001400

50 100 200 300 400 500Resp

on

se

tim

e (

R)

in

ms, re

ad

-on

ly in

ten

siv

e CMP Predicted

CMP Measured

RM Predicted

RM Measured

OCC Predicted

OCC Measured

... ...

...

C lien ts

R eq u es t q u eu e C o n ta in er q u eu e J M S S er v er

m 1 m '

M D B q u eu e

m 2

D ataS o u r c e q u eu e

C losed Q u eu e O pen Q u eu e

... ...

...

C lien ts

R eq u es t q u eu e C o n ta in er q u eu e J M S S er v er

m 1 m '

M D B q u eu e

m 2

D ataS o u r c e q u eu e

C losed Q u eu e O pen Q u eu e

Performance Prediction

Client SessionBean EntityHome EntityBean

setValuefindByPrimaryKey

getValue

setValue

getValueSet findByNonPrimaryKey

getAllValues

* [ fo r eac h en tity ]

Architecture model(calibrating)

Application design model

Performance model(populating)

Performance profile(benchmarking)

Performance model

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

stochasticprocess

statistics

From models to applications

Modelsqueuingtheory

middleware

softarch.

webtech.

Mission critical system

System integration and SOAs

Internet/Web applications

10

Research mission

Devising analysis models, architectures and frameworks to improve the performance and dependability of large distributed software systems.

11

Software architecture evaluation

stochasticprocess

statistics

Modelsqueuingtheory

middleware

softarch.

webtech.

Middleware Architecture Evaluation MethodS

How to evaluate the COTS software framework acquired?

How to evaluate the COTS software framework acquired?

Defence applications

12

Application in mission critical systems

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9Perforamnce

Scalability

LivenessConfigurability

Modifiability

System Under Test

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

stochasticprocess

statistics

Modelsqueuingtheory

middleware

softarch.

webtech.

Integrated SOAs

Internet

Net medical expenses over

$1500?

Retrieve Medicare Financial Tax

Statement

Proceed to the next section

Medicare Financial Tax

Statement Tax Office Medical Tax Statement Retrieval Web Service

Yes

No

egovernment Performance Assessment for Service Architecture (ePASA)

Can the system scale up to handle peak load at the deadline?

Can the system scale up to handle peak load at the deadline?

15

Application in SOAsRequest Distribution in Hour (01/07/2006-19/07/2006)

020406080

100120140160180200220240260280300320340360380

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Day

Re

qu

es

ts

01234567891011121314151617181920212223

S e s s io n s f ro mA TO e -Ta x

de dica te dn e two rk

A pplica t io n S e rv e r C lu s te r

D B s e rv e rs

m

Pro x y

M a in fra m e tra n s a ct io npro ce s s in g

m

Scenarios (i.e. 5 classes of workload)

Scenarios (i.e. 5 classes of workload)

Component(QNM equivalent

server)

Component(QNM equivalent

server)

Container(software hosting the

computing)

Container(software hosting the

computing) Host(physical deployment)

Host(physical deployment) Service demand

(e.g. CPU, Disk, network demand)

Service demand(e.g. CPU, Disk, network

demand)

Workload mixWorkload mix

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

Respond Sense

AnalysePlan

stochasticprocess

statistics

Modelsqueuingtheory

middleware

softarch.

webtech.

Adaptive Middleware Platform

(AMP)

Can models drive the adaptation? And how?

Can models drive the adaptation? And how?

18

Application in self-managing applications (1/3)

Multi-class token 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

stochasticprocess

statistics

Microkernel-based embedded systems

Modelsqueuingtheory

middleware

softarch.

webtech.

http://www.ok-labs.com/

Can low level OS libraries be modules and components?

Can low level OS libraries be modules and components?

23

Application in embedded OS

1

3

2

4

5

6

24

Application in embedded OS

Verifying CAmkES components and connectors

Client Server

uses providesIguanaRPC

“add” “add”CAmkES

PnP

send interface

receive interface send interface

receive interface

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

stochasticprocess

statistics

Modelsqueuingtheory

middleware

softarch.

webtech.

MarketModels

Resource allocation, valuation in Ultra Large Scale Systems (ULSS)

28

Applying market-based approach to ULSS

29

Teaching Experience

30

31

Course teaching Lecturer of Architecture of Software Systems–

COMP 9117, July 2006

School of Computer Science and Engineering, University of New South Wales

4th year software engineering degree undergrads, and postgrads

Design pattern, component-based development, services, software architecture and framework, AOP, model driven development

32

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)

33

Example student projects

34

Supervision statistics

35

Position Expectation

36

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 collaborationSupervision of postgrad students