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S-Cube Learning Package

Quality of Service-Aware Service Composition:

QoS optimization in service-based processes

Politecnico di Milano (POLIMI)

Barbara Pernici

Learning Package Categorization

S-Cube

Quality Definition, Negotiation

and Assurance

Quality Definition and SLA Negotiation

Quality of Service-Aware Service Composition: QoS optimization in service-based processes

© B. Pernici

Learning Package Overview

Problem Description

QoS optimization

Service selection

Optimization of composed services

Rebinding

Discussion

Conclusions

© B. Pernici

The Need for QoS Information in the Service-Oriented Architecture

Service Provider

(Supplier, Server)

Service Consumer

(Requester, Client)

1. Publish It differentiates

from competitors

Service Registry (Broker,

Directory)

2. Find It determines

the best match

3. Bind It is used for management (monitoring and control)

Many WSes with

similar functionality

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S1.op1

S1.op2

S1.op3

S2.op1

S2.op2

S2.op3

Service

registry

Adaptive

networks

Flexible e-services

Dynamic service

selection and

optimization

Adaptive front-

ends

Context-

awareness and

personalization

MAIS-Platform Scenario (MAIS-P)

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[B. Pernici, ed. Springer, Mobile Information Systems, April 2006]

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Multi-channel ADAPTIVE information systems

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Multi-channel ADAPTIVE information systems

t

QoS

Accepted quality threshold

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QoS optimization

Definition of the problem

Design or run time problem?

Process optimization

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Web Services

Elementary

Composed

How to associate QoS to WS definition?

Satisfaction of request

[Comuzzi, Pernici 2009]

=> Single values are considered (worst case) in most research work

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Registry: MAIS E-service ontology

QoS

[Bianchini et al, Ing. Sys., 2006]

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QoS

QoS

QoS QoS

QoS QoS

Receive

find-travel

Receive

book-travel

Travel-Service book-travel

find-travel

pay-travel

Receive

pay-travel

Service Interface

(+ Port types)

behavior

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WS QoS (an example)

<?xml version="1.0" ?>

<webServices>

<webService name="ws_1">

<maxTime value="6.767389" />

<minTime value="5.767389" />

<price value="0.123061776" />

<reputation value="0.82627237" />

<availability value="0.9903379" />

<dataQuality value="0.9857625" />

</webService>

<webService name="ws_2">

<maxTime value="7.9125476" />

<minTime value="5.767389" />

<price value="0.45754516" />

<reputation value="0.9279902" />

<availability value="0.9791445" />

<dataQuality value="0.91493726" />

</webService>

.

.

.

</webServices>

Candidate WSs

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Satisfaction of request

[Comuzzi, Pernici 2009]

=> Single values are considered (worst case) in most research work

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Quality global constraints:

cost <1000

train.reservation.cost<600

Invoke

hotel.reservation

Invoke

train.reservation

Preferred:

- ACMEHotels

- ItalianHotels

Negotiate:

- lowest price

- offer request

Invoke

flight.reservation

not late late

Probability=0.8 Probability=0.2

Composed WS

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Abstract process

op1

op2

op3

AS2

Abstract services

op1

op2

op3

AS1

Process

AS1.op1

AS1.op2

AS1.op3

AS2.op1

AS2.op2

AS2.op3

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Concrete process

op1

op2

op3

CS2

Concrete services Process Concretization

CS1.op1

CS1.op2

CS1.op3

CS2.op1

CS2.op2

CS2.op3

op1

op2

op3

CS1

op3

AS1.op1

AS1.op2

AS1.op3

AS2.op1

AS2.op2

AS2.op3

Process

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t1 t2 tI

ws1

ws1,1 ws1,2 ws1,|OP(1)|

. . .

...

ws2

ws2,2 ws2,|OP(2)| ...

wsJ

wsJ,1 wsJ,|OP(J)| ...

. . .

wsJ,2 ws2,1

A selection problem?

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Design time or run time problem?

When are service selected?

When is quality agreed?

Rebinding and renegotiation

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Flexible process Concrete services

T1

T4

T2 T3

Candidates for

T1

Candidates for

T2

Candidates for

T3

Candidate for

T4

Search

criteria

Search

criteria

Search

criteria

Global process constraints

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PAWS Framework & Architecture

[Ardagna et al IEEE Software 2007]

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Local optimization: run time selection of the best candidate

service which supports the execution of the running high level

activity

Global optimization: identification of the set of candidate

services which satisfy the end user preferences for the whole

application

Quality of Service (QoS) constraints at local and global level

WS Selection is an Optimization Problem

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An optimization problem?

Local optimization (Cardoso)

Linear programming (Benatallah, Ardagna)

Genetic algorithms (Canfora)

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QoS aggregate functions

Modified to account

for # of loop iterations

[Cardoso, 2005] Definition of a QoS evaluation function for each

pair (QoS attribute, workflow construct) and collapsing nodes

while evaluating

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Examples

S1

S2

S1 S2

S2S1

cost=2

avail=0.9

cost=3

avail=0.8

cost=2+3=5

avail=0.9 · 0.8=0.72

70% 30%

cost=2

avail=0.9

cost=3

avail=0.8

cost=0.3 · 2+0.7 · 3=2.7

avail=0.3 · 0.9+0.7 · 0.8=0.83

cost=2

avail 0.9

Estimated

Iterations=5

cost=2 · 5=10

avail=0.95=0.59

S1

cost=2

avail 0.9

r.time=3

cost=3

avail=0.8

r.time=2

Sequence Loop

Fork (flow) Switch

cost=2+3=5

avail=0.9 · 0.8=0.72

r.time=max(3,2)=3 [Cardoso, 2005]

© B. Pernici

Quality Driven composition

Proposes a global approach to planning to optimally select

component services during the execution of a composite service

Defines a quality model and a quality-driven service selection

[Zeng, et al., 2004]

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Quality Driven composition

Given a composite service

the execution path and the

execution plan are defined

– Execution paths are

sequences of states

– Execution plans are

execution paths in which

each activity is

performed by a service

The problem is to select

for each activity the

service that not only

satisfies local

requirements but also the

global ones

[Zeng, et al., 2004]

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Hot path

[Zeng et al, 2004] Exploring all paths and computing the overall

QoS on a “hot” path

– Considering each path at a time

– Only paths likely to be executed (hottest paths)

– Unfolding cycles

– Global planning with linear programming solution

- Difficult to handle attributes that aggregate non-linearly

[Zeng, et al., 2004]

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Normalization [Zeng et al.]

Given an attribute Q, find the maximum QMAX and minimum QMIN

across all the available services

The normalized value V will be:

and the fitness (to be minimized):

1

QQ

QQ

V MINMAX

MIN

1

QQ

QQ

V MINMAX

MAX If QMAX≠QMIN

If QMAX=QMIN

If QMAX≠QMIN

If QMAX=QMIN

negative attributes Positive attributes

e.g. cost e.g. availability

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Unfolding loops

– Useful in presence of

constraints

– In case of loops, each

abstract service invocation

may be assigned to

different concrete services

– Objective: meet constraints

and maximize QoS

– Loops are unfolded

S1

3

S1

S1

S1

C RT

CS1 3 2

CS2 5 1

Fitness=C+RT

RT≤5

Fitness=18 Fitness=16

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Composed services optimization

[Ardagna, Pernici, 2005, 2007] Global and Local QoS Constraints

Guarantee in Web Service Selection

Loops:

– Unfolding (consider maximum number of iterations)

– Peeling (associate probability to each iteration sequence)

Global constraints enforcement

– Only Critical path in Zeng 2004

– Local optimization in Meteor-S

Reoptimization (Adaptivity)

Negotiation in case of unsolvable problem

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[Ardagna and Pernici, 2007]

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[Ardagna and Pernici, 2007]

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Formal Definitions

Let wsi,j be the i-th Web service candidate for the execution of task tj

Execution Path (epl): a set of tasks {t1,t2,…,tn}, such that t1 is the initial task, tn is the final task and no ti, tj belong to alternative branches

Sub path (spml): a sequence of tasks [t1,t2,…, tn], ti epl from the begin to the end

task which does not contain any parallel sequence

Execution Plan (eplkl): a set of ordered couples (tj, wsī,j), indicating that task tj

included in eplkl is executed by a given WS wsī,j

Global Plan: a set of ordered couples (tj, wsī,j), which associates every task tj to a given WS wsī,j and satisfies local and global constraints for all execution paths

© B. Pernici

An Example

Global constraints:

– Price6$

– Execution time7 sec.

Solution: ws2,1, ws2,2 and ws2,3 (no global constraints violation in all cases)

© B. Pernici

Notation

Symbol Description

ei,j wsi,j execution time

ai,j wsi,j availability

pi,j price for wsi,j execution

ri,j wsi,j reputation

WSj set of indexes corresponding to candidate Web services of task tj

A set of tasks included in the composite service specification

Al set of tasks included in the execution path epl

L number of execution path arising from the composite service

specification

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Problem Formulation

The WSC problem is multi-objective and a simple additive

weighting technique is used to evaluate the overall value

of QoS from multiple quality dimensions

Decision variables:

– yi,j equals 1 if the Web service wsi,j executes task tj, 0 otherwise

– xj start time of task tj

– ej task tj duration

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Objective Function

Plan score:

– (other dimensions may be included, e.g. data quality)

Problem objective:

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[Ardagna and Pernici, 2007]

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Problem Constraints

Assignment constraints:

Task duration constraints:

[Ardagna and Pernici, 2007]

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Problem Constraints

Local constraints: e.g.

Execution time constraints:

Availability constraints:

Price constraints:

Reputation constraints:

The problem is NP-hard, it is equivalent to a Multiple choice

Multiple dimension Knapsack Problem

=> Log if not linear

[Ardagna and Pernici, 2007]

© B. Pernici

Another issue

Task dependency:

– Two tasks have to be executed by the same operation (or different

operations) of the same WS

– Eg: make reservation - payment

[Ardagna and Pernici, 2007]

© B. Pernici

Re-optimization

Re-optimization is performed:

– if the current QoS value differs from the corresponding prediction

(variability of performance of Web services)

– if a Web service invocation fails

– for end user 's context switch

– after the evaluation of switch conditions

– periodically with an adaptive time period Tp (new candidate

services)

Re-optimization reduces the number of variables of the problem and

can be computed efficiently

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[Ardagna and Pernici, 2007]

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Performance analysis

[Ardagna and Pernici, 2007]

© B. Pernici

Finding a solution using GA

Representation:

– Array chromosome

– A slot for each abstract service invoked in the process

– Value indicates the binding

– S1, …, SN abstract services

– CSi1, …, CSik concrete services for Si

S1 S2 SN

CS11

CS13

CS12

CS21

CS24

CSN1

CSN

k

[Canfora, Di Penta

2005]

© B. Pernici

Genetic Algorithms

Initialize population P[0];

g=0;

while(g < max_number_of_generations)

Evaluate P[g];

g=g+1;

Select P[g] from P[g-1];

Crossover P[g];

Mutate P[g];

end while

[Canfora, Di Penta

2005]

© B. Pernici

GA Operators

- Selection: Roulette wheel

– pcross=0.7

– pmut=0.1

– GA type: Simple with elitism of 2 (best) individuals

– Population size: 100 individuals

CS13

CS33

CS41

CS41

CS11

CS24

CS31

CS44

CS13

CS22

CS31

CS44

CS11

CS24

CS22

CS33

CS13

CS41

CS22

CS33

CS11

CS12

CS13

CS21

CS22

CS23

CS24

CS31

CS32

CS33

CS41

CS42

CS43

CS44

Crossover: Mutation:

[Canfora, Di Penta

2005]

© B. Pernici

Optimization Problem

Given a solution s

Let us suppose we have the SLA constraints in the form

e.g.

The fitness function (to be minimized) would be something like:

or

where D(s) is defined as:

)s(Dw)s(liabilityRew)s(tyAvailabiliw

)s(sponseTimeRew)s(Costw)s(F

5

43

21

020)s(sponseTimeRe

0100)s(Cost

n...1i0)s(cli

i

n

iy)s(cl)s(D

0)s(cl1y

0)s(cl0y

ii

ii

)s(D1)s(liabilityRew)s(tyAvailabiliw

)s(sponseTimeRew)s(Costw)s(F

43

21

[Canfora, Di Penta

2005]

© B. Pernici

Comparing GAs with integer programming

– Integer Programming (IP) is one of the most widely adopted techniques for this kind of problems

– IP model depends on the number of concrete services available

– Given a workflow composed of S1…Sn abstract services, each one bound to CSi,1,…CSi,m concrete service,

- The number of variables yi,j in the IP problem is given by:

1,0y

1y

j,i

m

1j j,i

i

n

1i

im

[Canfora, Di Penta

2005]

© B. Pernici

Comparing GAs with IP: Results of an empirical study

GAs better than IP for # of concrete services >17

GAs ensure better scalability

Also, it allows to handle non linearity

Workflow containing 18

invocations of 8 distinct abstract

services, cyclomatic

complexity=5

– # of concrete services per

abstract service varying

between 5 and 25

[Canfora, Di Penta

2005]

© B. Pernici

Discussion

IP is a widely adopted technique to solve this kind of problem

GAs better scale up when the number of available concrete services for an

abstract service is very high

– Possible scenarios: widely used services (e.g. hotel booking, search URL, etc.)

GAs do not pose any limitation on the linearity of the fitness function and of

the QoS aggregation formulae

– Linearization necessary for IP not to consider for user-defined, domain

specific QoS attributes

- e.g. temperature service may have QoS attributes such as refresh rate,

accuracy, etc.

– Alternative: nonlinear IP serious scalability problems

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Handling re-binding

[Canfora 2005]

© B. Pernici

Causes of re-binding

A service may not be available

or, better services can be available

QoS values deviate from the estimate

Unlikely paths are followed

– Branches unlikely to be executed

– # of iterations largely different from the estimated value

This may lead to:

– Impossibility to continue the execution

– Constraint violation

– Poor optimization of the objective function

[Canfora 2005]

© B. Pernici

Triggering Service re-binding

Perform re-binding between abstract and concrete services

When:

– QoSestimated >QoSconstraint

or QoSactual- QoSestimated >threshold

Due to one of the following events

1. Difference between estimated and actual number of loop iterations

2. Choice of the path to be followed in a switch

3. Difference between the estimated and actual QoS of some executed services

4. A service is not available

The algorithm will try to trigger the re-binding once enough information is available

[Canfora 2005]

© B. Pernici

Remaining n3 iterations: k’3-j3

Remaining n2 iterations: k’2-j2

Determining the re-binding slice

[Canfora 2005]

© B. Pernici

Rebinding: response time evolution

0

5

10

15

20

25

30

35

initial estimate replan. value new estimate final value

Resp

on

se T

ime [

s]

T<29

T<31

[Canfora 2005]

© B. Pernici

Re-binding may fail…

No service available for replacement

No way to recover from constraint violation

– e.g. timing constraints already violated

No way to optimize the objective function

What to do

– Suspend the execution

- replace the unavailable service

– Terminate the execution

- Nothing can be done

– Continue anyway

- Constraints not so hard

- Try to limit the violation

– (Re)Negotiate (Ardagna-Comuzzi-Pernici)

© B. Pernici

Discussion and future work

Interrelation between design and run time decisions (design for QoS optimization),complexity

Semantic information about quality (are weights enough?)

Incomplete information and distributed decisions

Variable quality profiles

Multiple instances and multiple processes

Link with strategic goals and underlying infrastrucure; should decisions be taken independently

Stability of solutions

Application areas

Soft and hard constraints

Further S-Cube Reading

Marco Comuzzi, Barbara Pernici: A framework for QoS-based Web service

contracting. TWEB 3(3): (2009).

V. Andrikopoulos, M.G. Fugini, M. Papazoglou, M. Parkin, B. Pernici, B., S.H.

Siadat, QoS Contract Formation and Evolution, 11th International Conference on

Electronic Commerce and Web Technologies (EC-Web 2010)

George Baryannis, Olha Danylevych, Dimka Karastoyanova, Kyriakos

Kritikos, Philipp Leitner, Florian Rosenberg, Branimir Wetzstein,

Service Composition,

in Service Research Challenges and Solutions for the Future Internet:

S-Cube – Towards Engineering, Managing and Adapting Service-Based System,

Mike P. Papazoglou, Klaus Pohl, Michael Parkin and Andreas Metzger (eds), LNCS

6500, 2010

Salima Benbernou, Ivona Brandic, Cinzia Cappiello, Manuel Carro,

Marco Comuzzi,, Attila Kertesz, Kyriakos Kritikos, Barbara Pernici,

Pierluigi Plebani,

Modeling and negotiating service quality,

in Service Research Challenges and Solutions for the Future Internet:

S-Cube – Towards Engineering, Managing and Adapting Service-Based System,

Mike P. Papazoglou, Klaus Pohl, Michael Parkin and Andreas Metzger (eds), LNCS 6500, 2010

© B. Pernici

© B. Pernici

References

D. Ardagna and B. Pernici. Adaptive service composition in flexible processes. IEEE Trans. on

Software Engineering, June 2007.

D. Ardagna, M. Comuzzi, B. Pernici, P. Plebani, PAWS: Processes with adaptive web services, IEEE

Software, Nov. 2007

D. Bianchini, V. De Antonellis, B. Pernici, P. Plebani: Ontology-based methodology for e-service

discovery. Inf. Syst. 31(4-5): 361-380 (2006)

G. Canfora, M.di Penta, R. Esposito, and M. L. Villani. QoS-Aware Replanning of Composite Web

Services. ICWS 2005 Proc., 2005. Orlando.

J. Cardoso. Quality of Service and Semantic Composition of Workflows. Ph. D. Thesis, Univ. of

Georgia, 2002.

S. Chandrasekaran, J. A. Miller, G. Silver, I. B. Arpinar, and A. P. Sheth. Performance Analysis and

Simulation of CompositeWeb Services. Electronic Market: The Intl. Journal of Electronic

Commerce and Business Media, 13(2):120–132, 2003.

D. B. Claro, P. Albers, and J. K. Hao. Selecting Web Services for Optimal Composition. In ICWS 2005

Workshop Proc., 2005. Orlando.

M. C. Jaeger, G. Muhl, and S. Golze. QoS-aware composition of web services: An evaluation of

selection algorithms. In COOPIS 2005 Proc., 2005. Cyprus.

B. Pernici, ed. Springer , Mobile Information Systems, April 2006

Tao Yu, Yue Zhang, and Kwei-Jay Lin. Efficient algorithms for web services selection with end-to-end

QoS constraints. ACM Trans. Web, 1(1):1–26, 2007.

L. Zeng, B. Benatallah, M. Dumas, J. Kalagnamam, and H. Chang. QoS aware middleware for web

services composition. IEEE Trans. on Software Engineering, 30(5), May 2004.

Acknowledgements

The research leading to these results has

received funding from the European

Community’s Seventh Framework

Programme [FP7/2007-2013] under grant

agreement 215483 (S-Cube).

© B. Pernici