S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection
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Transcript of S-CUBE LP: A Soft-Constraint Based Approach to QoS-Aware Service Selection
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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).