s-cube lp: a soft-constraint based approach to qos-aware service selection

www.s-cube-network.eu

S-Cube Learning Package

A Soft-Constraint Based Approach to QoS-Aware Service Selection

Université Paris-DESCARTES

Mohamed-Anis ZEMNI, Salima BENBERNOU, Manuel CARRO

Learning Package Categorization

S-Cube

Quality Definition,

Negotiation and Assurance

Quality Management and Prediction

Analysis Operations on SLAs:

Detecting and Explaining Conflicting SLAs

Service Selection and QoS

Service selection is the first step to improve service

composition within Service-Oriented-Architecture (SOA):

• Searches for services fitting users’ requirements

• Explores services’ properties

• Aims at putting together several elementary services

• Generates new value-added service

Quality of Service (QoS) for selection often critically important:

• Software services expose not only functional characteristics, but also

non-functional attributes describing their QoS

• Defines the service level (Key Performance Indicator)

• A service fulfilling all the functionality but with low QoS is not

interesting

Learning Package Overview

Problem Description

Extending SCSP with Penalties & new SLA Model

Conclusions

Problem Description: Service Selection Scenario

User request (criteria)

Select only one service

among the available

services that have the

same functionalities but

with different QoS

1

2

Used Approach at Design-time

Functionalities

+

QoS

Problem Description: Service Selection Techniques in the Literature

Constraint Satisfaction Problem (CSP):

• Classical formulation of constraints

• Quite expressive to represent several real life problems

• Defines a set of variables, each of them ranging on a finite domain,

and a set of constraints restricting the values that these variables can

take simultaneously

• All the constraints must be satisfied simultaneously

Lack of built-in capabilities to express preferences among constraints

and the lack of possibility of giving approximate solutions for problems

which are overconstrained

1

Problem Description: Service Selection Techniques in the Literature

Soft Constraint Satisfaction Problem (SCSP)

• Include the concept of preferences into every constraint in order to

obtain a suitable solution which can be optimal or, in general, a

reasonable estimation, maybe at the expense of not fulfilling all

constraints

• Relies on composing the constraints in order to obtain the optimal

solution

• Applied to the requirements (in terms of preferences) of the users

Only one solution returned that is optimal

* Stefano Bistarelli, Ugo Montanari, and Francesca Rossi. Semiring-

based constraint satisfaction and optimization. J. ACM, 44(2):201–

236, 1997

1

Problem Description: Service Selection Techniques in the Literature 1

Example : Searching for services Available at y% of the time and with reputation = z

C-semi-ring : Algebraic structure

Only one domain for

all variables

Problem Description: Problem at Design-time

User request (criteria)

1. Required criteria

cannot match any service!!!

2. I have to fix

new criteria

Problem Description: Problem at Runtime

Some problems, encountered by the service may

lead to service malfunctions

contract violation

activity interrupted,

must apply penalty!!!

• Advertising the quality level of the services

• Taking note about the user preferences

• …”

SLA - Definition:

“An XML document and a contract for…

Problem Description: SLA

I want an SLA

ensuring the

performances I

am searching for

Problem Description: Problem at Runtime

Where are

My preferences

and the penalties?

2

Learning Package Overview

Problem Description

Extending SCSP with Penalties & new SLA Model

Conclusions

Main Objective

User request (preferences,

penalties) …

Automatically switch from a faulty

service to a new one

Design-time Runtime

Approach Main Points

Definition of Soft Service Level Agreement (SSLA) an SLA

model extended with preferences and penalties

Extension of Soft Constraint Solving Problem handling

penalties: Define in SSLA the penalty artifacts, such that, if a

selected service failed, another one should replace it that

fitting with the agreed QoS in the contract with penalties if

some of them are not fulfilled

SSLA to SCSP mapping

Kinds of penalties

Arithmetical Penalties

• In relation with measurable qualities of service

• Direct relation to service variables

• E.g. availability, the response time, the reputation, etc.

• The application of arithmetical penalties is a consequence of a

contract breach and therefore the transition to a different selection

using the choices expressed by the customer in the form of

preferences

Behavioural Penalties

• Related to the behavior of either the customer or the service provider

• The application of behavioral penalties is not always a consequence of

a contract breach and so, switching to another choice is not obligatory

and even less replacing the service

Soft SLA Definition

Soft SLA Definition: Preferences & Penalties

I prefer to get a payment

service and delivery service

having response time < 5ms. I

also accept services with

response time between 5ms

and 20ms with preference =0,5

Etc.

Response time

Preferences

<5ms

[5ms,20ms[

>20ms

If the first

preference is not

fulfilled during the

execution I would

apply penalty P7

Most preferred

Less preferred

Soft SLA Definition

Guarantee terms are expressed in terms of preferences and

penalties

• Preferences are ranked (most preferred to less preferred)

• Penalties are applied if a preference is not fulfilled

The service broker search for service fulfilling the QoS from

the most preferred to the less preferred (at design-time)

Penalties are applied only at runtime and never at design-

time, on the faulty service

QoS

variables Variable

doamins

Preference

degree

Preferences Penalties Preferences/Penalties

association

SSLA document

Extending SCSP Using Penalties

Constraint

System

Constraints

Operations

Solution

SCSP

Extending Constraint System

Constraint

System

Constraints

Operations

Solution

SCSP

CS = <S; D{}; V>

S = algebraic structure

including preference

values

V = QoS variables

D{} = Variable domains

Penalties into S

Extending Constraints Using Penalties

Constraint

System

Constraints

Operations

Solution

SCSP

Def = Definition of the

constraint in terms of

preference value

Type = in terms of

variable intervening in

the constraint

Penalties into Def

Rewrite operations Logic

Constraint

System

Constraints

Operations

Solution

SCSP

Combination =

combination of the

constraints (pref)

Projection = generates

the optimal solution

Combination of penalties

Rank generated

solutions and

keep them all

Extending SCSP Using Penalties

Global Preferences

+

-

Most preferred

Less preferred

Constraint

System

Constraints

Operations

Solution

SCSP

Penalty based SCSP Case Study

Constraint

System

Constraints

Operations

Solutions

Penalty based SCSP

Pn = Penalty values

[0, 1] = Preference values

V = {responseTime, coSt,

Availability, Reputation}

Penalty based SCSP Case Study

Constraint

System

Constraints

Operations

Solutions

Penalty based SCSP

Penalty based SCSP Case Study

Constraint

System

Constraints

Operations

Solutions

Penalty based SCSP

Penalty based SCSP Case Study

Constraint

System

Constraints

Operations

Solutions

Penalty based SCSP

Proposed Approach Logic

Input: Constraints, penalties, table of constraint definitions

Output: Choices with their possible alternatives ordered

Begin

For each selection alternative do

Combine all the constraints together (apply the min operator);

End for;

Order the results according to preference values into groups;

For each preference value group do

Order the elements corresponding to the penalty value;

End for;

End;

Mapping SSLA onto SCSP Solvers

Learning Package Overview

Problem Description

Extending SCSP with Penalties & new SLA Model

Conclusions

Conclusions

1. Soft constraint-based framework

2. Express QoS properties reflecting both customer

preferences and penalties applied to unfitting situations

3. Solution for overconstrained problems

– The application of soft constraints makes it possible to work around

overconstrained problems and offer a feasible solution

4. Provide ranked choice to offer more flexibility at design-time

to find required services, and at runtime to ensure users’

rights

5. Concept of penalties in SCSP

We plan to extend this framework to also deal with

behavioral penalties

References

This presentation is based on [ZBC10]

Further S-Cube Reading

[ZBC10] Mohamed Anis Zemni, Salima Benbernou, and

Manuel Carro

A Soft Constraint-Based Approach to QoS-Aware

Service Selection

In proceeding of the Service-Oriented Computing -

8th International Conference (ICSOC 2010),

volume 6470 of Lecture Notes in Computer

Science, pages 596-602 San Francisco, CA, USA,

December 7-10, 2010

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).