Managing QoS Acceptability for Service
Selection : A Probabilistic Description Logics
Based Approach
Salima Benbernou1 , Allel Hadjali 2 , Naouel Karam3 , Mourad Ouziri1
1
Université Paris Descartes, Sorbonnes Paris Cité, France
{mourad.ouziri,salima.benbernou}@parisdescartes.fr
2
LIAS/ENSMA, Poitiers, France
allel.hadjali@ensma.fr ,
3
Department of Mathematics and Computer Science, Freie Universität Berlin,
Germany
naouel.karam@fu-berlin.de
Abstract. Quality of Service (QoS) guarantees are usually regulated
in a Service Level Agreement (SLA) between provider and consumer
of services. Such guarantees are often violated, it may however be the
case where the available services do not match exactly all required QoS,
leading the system to grind to a halt. It would be better to look for an
approximation for acceptable QoS and avoid the complete stopping of the
running services. This paper aims at making dynamic QoS acceptability
easy for service selection. The proposed model is based on an extension
of existing probabilistic description logics reacting to QoS variations.
The contributions made are twofold (1) a query description language to
express the required QoS by means of a probabilistic description logic
(2) a reasoning algorithm for decision making about the acceptability of
QoS w.r.t the probabilistic description.
1 Introduction
In a Service Oriented Architecture, service providers need to characterize their
services defining both the offered functionalities and the offered quality. Then,
the quality of service (QoS) properties can be critical elements for achieving the
business goals of service providers. Establishing QoS contracts, described in a
SLA, that can be monitored at runtime, is therefore of paramount importance.
The SLA must stipulate the values of QoS attributes that a service provider is
expected to deliver to a client.
Moreover, a successful execution of a service composition implies continuous
monitoring of QoS at runtime. However, at this step, variations could occur
in the QoS and most likely induce violations of the agreement. For that, the
service composition should support a dynamic QoS-driven adaptation. A static
adaptation is not adequate due to variations of the QoS. Existing approaches
deal with static QoS, they do not address the attribute variations issue. A formal
and declarative approach to achieve a dynamic adaptation is then highly desired.
In this paper, we propose a probabilistic description logic based approach to
describe the QoS attributes and handle their variations through the use of lin-
guistic concepts. The proposed approach helps to select services in order to build
approximate compositions which may not satisfy all the requirements, hence
avoiding to completely stop running the processes. It provides the basis allowing
the representation and reasoning about the introduced symbolic concepts, where
the variation of QoS is modeled in a probabilistic way, hence managing the QoS
acceptability. Our main contributions are summarized in the following:
1. We introduce a query description language to express the required QoS using
both linguistic concepts and probabilistic representations. To this end, we
extend existing probabilistic description logics to handle QoS variations.
2. We develop a suitable reasoning algorithm for computing the QoS acceptabil-
ity of the selected service w.r.t the probabilistic description. The algorithm
achieves an approximate reasoning using inference rules involving probabilis-
tic statements.
The remainder of the paper is structured as follows. In section 2, we provide
first, a discussion about existing works dealing with QoS and, second a review of
the approaches related to probabilistic description logics. Section 3 describes our
proposal to manage the QoS acceptability. In section 4, we present the syntax and
semantics of the proposed language. Section 5 details the reasoning algorithm
and section 6 concludes the paper.
2 Related Work
Most of existing approaches focused on the contract definition and on mecha-
nisms for contract enactment. [1] describes a matchmaking algorithm for rank-
ing functionally equivalent services. In [8], a fuzzy service adaptation approach
that leverages the degrees of QoS satisfaction, is discussed. [11] proposes a soft
constraint-based framework to seamlessly express QoS properties reflecting both
customer preferences and penalties applied to unfitting situations. The work in
[2] discusses a constraint based approach for quality assurance in a choreogra-
phy system. In [10], a new trend called service skyline computation for handling
multiple quality criteria including user preferences, is proposed.
On the other hand, semantic technologies have been applied in recent works
for reasoning about QoS of different systems during service composition. [7] pro-
poses a meta-model for non functional property descriptions targeted to support
the selection of Web services. The contribution in [3] focuses on the analysis
of the requirements for a semantically rich QoS-based Web Service Description
Model and an accurate, effective QoS-based WS Discovery (WSDi) process. [9]
presents an overview of prominent research works related to developing QoS
ontologies.
It is worthy to note that the above approaches do not handle the varia-
tions of QoS at runtime. An effective service composition management requires
a support for the acceptance of QoS values, and should also allow for a flexible
acceptability for autonomous corrective actions. We provide here a probabilistic
description logic based approach allowing self-healing in reaction to QoS varia-
tions by expressing their semantics.
In the literature, a number of works on probabilistic description logics exists
[6, 5, 4]. In cite[4], the authors proposed a probabilistic extension of the expressive
description logics P-SHIF(D) and P-SHOIN (D) that encompasses both statis-
tical and subjective features, and also addresses the non-monotonic aspects of
probabilistic knowledge using a semantics based on Lehmanns lexicographic en-
tailment. In the cited approaches, the terminological and assertional knowledge is
extended with conditional constraints and refered to as probabilistic knowledge.
Those constraints express probabilities relating concepts or individual assertions.
Our approach describes probabilities in a higher level of abstraction which allows
to define probabilistic concepts that can be used in complex descriptions. We
provide a reasoning algorithm dealing with such concepts.
3 Dynamic QoS Acceptability Framework
As depicted in Figure 1, our work can be integrated into a service composition
system as a real-time QoS acceptability process. The inputs are the QoS required
by the service selection process and the events captured by the monitoring pro-
cess. The output is a set of acceptable services regarding their effective QoS.
Events dealing with real-time execution of services are captured by the mon-
Service Composition Framework
QoS selection process
SLA Required QoS
Probabilistic QoS Probabilsitic description
QoS Requirements Selection process
Probabilistic reasoning
Acceptable services Probabilistic QoS
w.r.t. QoS
Service composition Probablistic calculus
process
Interpreted events
Symbolic encoding
Services
process
events
execution results
Service execution
Monitoring
process
Fig. 1. QoS variations framework for service selection
itoring process. From these events, we extract real-time measures of the QoS
provided by services. To efficiently handle such measures for the purpose of de-
cision making, they are expressed thanks to symbolic concepts while taking into
account their variations in a probabilistic way. We illustrate this idea through
the following example.
Let us consider two services S1 and S2 involved in a web services composition.
Assume that the monitoring process has captured the events given in Table 1.
Event Execution time Service ID Response time (TR) RAM Consuming (RC)
1 10:15:45 S1 12ms 3MB
2 10:55:05 S1 3ms 11MB
3 21:30:00 S1 2ms 4MB
4 10:15:45 S1 4ms 13MB
5 21:35:55 S2 1ms 80MB
6 23:10:09 S2 2ms 75MB
Table 1. Example of events captured by the monitoring process
3.1 Symbolic encoding of the events
Each quantitative measure captured by the monitoring process is interpreted
by symbolic value using business rules. Business rules may be provided by the
application domain experts in a Service Level Agreement (SLA). In what follow
are examples of business rules:
– Rule 1 (for the interpretation of Time Response measures): A response time
is said to be good (GoodTR) if it is less than 3ms, medium (MediumTR) if
it is between 4ms and 6ms and bad (BadTR) otherwise.
– Rule 2 (for the interpretation of RAM consuming measures): A RAM con-
suming is said to be good (GoodRC) if it is less than 5Mb, bad (BadRC)
otherwise.
Given the above two rules, the measures of Time Response and RAM are shown
in Table 1 can be expressed in the following qualitative/symbolic descriptions:
– Event 1: S1 : BadTR, S1 : GoodRC.
– Event 2: S1 : GoodTR, S1 : BadRC.
– Event 3: S1 : GoodTR, S1 : GoodRC.
– Event 4: S1 : MiddleTR, S1 : BadRC.
– Event 5: S2 : GoodTR, S2 : BadRC.
– Event 6: S2 : GoodTR, S2 : BadRC.
3.2 Probabilistic QoS
The real-time QoS of a given service is not static but varies over different execu-
tions of the service. The QoS of each service has to be represented by a unique
description in terms of a probability distribution 1 . Hence, the multiple values
about a symbolic QoS are described using the Bayesian probability defined as
follows:
P (SymbQoS|Si ) = P (SymbQoS∩S
P (Si )
i)
= |SymbQoS∩S
|Si |
i|
= number of appearences of SymbQoS in the events of Si
N umber of executions of Si
where
SymbQoS ∈ {BadT R, GoodT R, M iddleT R, BadRC, GoodRC, M iddleRC}.
Back to the example introduced in section 3.1, the services S1 and S2 have
been executed four times and twice, respectively. Hence, the probability values
associated with their QoS can be computed as follows:
– S1 received once BadTR. So, S1 is BadTR with a probability of 0.25.
– S1 received three times GoodTR. So, S1 is GoodTR with a probability of
0.75.
– S1 received twice GoodRC. So, S1 is GoodRC with a probability of 0.5.
– S1 received twice BadRC. So, S1 is BadRC with a probability of 0.5.
– S2 received twice GoodTR. So, S2 is GoodTR with a probability of 1.
– S2 received twice BadRC. So, S2 is BadRC with a probability of 1.
3.3 Probabilistic QoS selection process
Given a required QoS, a traditional selection process decides whether a known
service is acceptable or not. In our case, the selection process operates in an
uncertain (probabilistic) environment since each QoS is described thanks to a
probability distribution.
To this end, we propose a formal model based on probabilistic DLs to pro-
cess acceptability of QoS. In this model, the required and provided QoS are
described using a formal description language. A reasoning algorithm to decide
the acceptability, is proposed as well.
As example for a required QoS, let’s consider the following query: Retrieve the
services that should provide a Good Response Time with a probability greater
than 0.7 and Bad RAM Consuming with a probability less than 0.55. Intuitively,
Only service S1 is acceptable with respect to this required QoS, the Service S2 is
not acceptable because it does not provide Bad RAM consuming with probability
less than 0.55.
1
The interpretation of probability considered here is the so-called ”the a-priori inter-
pretation” which is the oldest and simplest one. The probability of an event is the
number of favorable cases, where this event occurs, divided by the total numbers
of possibles cases. So, w.r.t the same service and the same attribute, the required
axiom that probabilities add up to 1 holds
In what follows, we present the proposed formal model for QoS acceptability
decision making.
4 Probabilistic DL for Handling QoS Acceptability
The model relies on a probabilistic extension of DL defined with the following
components:
– An expression language, including a set of symbols that are used to express
knowledge about QoS.
– A semantic of symbols of the expression language, based on an interpretation
domain and an interpretation function.
– A reasoning algorithm based on a set of inference rules to make a decision
about the QoS acceptability.
Syntax of the description language
The proposed syntax is summarized in Table 2 (with ◦ ∈ {<, >, ≤, ≥}). The in-
troduced description language allows expressing two types of information related
to QoS:
1. The knowledge about dynamic QoS, and
2. The information related to the required QoS.
Syntax Interpretation
Q◦p Refers to services that provide the QoS Q with a probability ◦ p (Q◦1 is noted Q)
Q1◦p u Q2◦q Refers to services that provide the QoS Q1 and Q2 with probability, respectively, ◦ p and ◦ q
Q1◦p t Q2◦q Refers to services that provide the QoS Q1 or Q2 with probability, ◦ p and ◦ q, respectively
¬Q◦p Refers to services that do not have the QoS Q with a probability ◦ p
Q1 v Q2 Means: Any service that provides the QoS Q1, then it provides automatically the QoS Q2.
Table 2. Syntax of the proposed description language
For instance, the dynamic QoS of the example provided in the previous sec-
tion can be written as follows:
BadT R0.25 (S1 ), GoodT R0.75 (S1 ), GoodRC0.5 (S1 ), BadRC0.5 (S1 ),
GoodT R1 (S2 ), BadRC1 (S2 ).
Similarly, the description of the required QoS can also be expressed using this
language. For example, the following formulas are two different descriptions of
possible required QoS:
GoodT R≥0.7 u GoodRC≥0.4
¬BadT R≥0.1 u GoodRC≥0.9
In the first, we require a good time response with a minimum rate of 70% and
good RAM Consuming with the minimum rate of 40%. While in the second one,
we require services that provide good RAM Consuming with probability greater
or equal than 90% and, at the same time, do not provide bad time response with
probability greater than 10%.
Formal semantics of the description language
The formal semantics of terms and constructors of the description language is
defined by the pair ∆I , .I where:
– ∆I is an interpretation domain. It is composed of individuals that represent
the services managed in the BPM (Business Process Management) system.
– .I is an interpretation function that assigns terms Q of the language to indi-
viduals of ∆I as shown in Table 4 (where ¯◦p stands for the complementary
¯ = ≤ p):
of ◦p, for instance >p
Syntax Formalinterpretation
Q◦p QI◦p = s ∈ ∆I | QI (s) ◦ p
Q1◦p u Q2◦q Q1I◦p u Q2I◦q = s ∈ ∆I | Q1I (s) ◦ p ∧ Q2I (s) ◦ q
Q1◦p t Q2◦q Q1I◦p t Q2I◦q = s ∈ ∆I | Q1I (s) ◦ p ∨ Q2I (s) ◦ q
¬Q◦p (¬Q)I◦p = QI◦¯p = s ∈ ∆I | QI (s) ¯◦p
Q1 v Q2 ∀s ∈ ∆I , Q1I (s) ⇒ Q2I (s)
Table 3. Formal semantics of the proposed probabilistic DL
For instance, the probabilistic concept Q≥0.5 is interpreted as the set of
individuals with a probability degree greater than 0.5. Interpretation of complex
and composed descriptions is also allowed as stated in Table 3.
5 Reasoning Algorithm for the Dynamic QoS
Acceptability Decision
Based on the syntax and semantics of the description language presented in
the previous section, we develop a reasoning algorithm that decides whether a
provided dynamic QoS is acceptable with respect to a required one.
The decision problem is formulated as follows:
”Is the dynamic QoS provided by the service Si acceptable with respect to a
given required QoS RQ ?”
This acceptability decision problem can be formalized by the following logical
deduction: h(T Box, ABox)i |= RQ(Si )
where (T Box, ABox) is the knowledge base with two components:
– A TBox: containing knowledges about the considered domain application.
For example, it may contain the following axioms:
{GoodT R1 v BadT R0 , BadRC1 v GoodRC0 }
– An ABox: containing knowledges about dynamic QoS provided by services.
For example, the ABox may contain the previous QoS of services S1 and S2 :
{BadT R0.25 (S1 ), GoodT R0.75 (S1 ), GoodRC0.5 (S1 ), BadRC0.5 (S1 ),
GoodT R1 (S2 ), BadRC1 (S2 )}
The logical deduction is achieved by checking the inconsistency of the follow-
ing knowledge base:
hT Box, ABox ∪ {¬RQ(Si )}i
The inference machinery calls on the propagation rules introduced in the
following sub-section.
5.1 Algorithm for QoS acceptability
A sample of the propagation rules that constitute the foundations of our prob-
abilistic DL is depicted in Figure 2. Those rules are defined with respect to the
formal semantics given in section 4. For sake of clarity, the Rreduction rules are
given for ≤ and ≥, one can easily deduce the corresponding rules for < and >.
The algorithm checks the consistency of the knowledge base:
hT Box, ABox ∪ ¬RQ(Si )i
by applying the propagation rules until termination. Each application of a prop-
agation rule generates a new inference system ISi . The algorithm terminates
if:
1. There exists a clash in the current inference system. In this case, the knowl-
edge base is not consistent which means that the checked QoS is acceptable.
2. No more rule can be applied to generate a new inference system. In this case,
the knowledge base is consistent, which means that the checked QoS is not
acceptable.
An inference system ISi contains clash if:
ISi = {Qp0 (s), p ≤ p0 }, or
ISi = {Q≤p (s), Q≥p0 (s), p < p0 }, or
ISi = {Q
p0 (s), p < p0 }, or
ISi = {Q=p (s), Q=p0 (s), p 6= p0 }
The algorithm is sound because for any satisfiable concept of the proposed
desciption logic, the application of propagation rules given in Fig. 2 terminates on
at least one state free of clash. By the same way, starting from an inconsistant
concept, all terminal states of the algorithm after applying propagation rules
contain clash.
Rule R1 :
ISi = {Q(s)} −→ ISi+1 = ISi ∪ {Q≥1 (s)}
Rule R0 :
ISi = {¬Q(s)} −→ ISi+1 = ISi ∪ {Q≤0 (s)}
Rule R¬ :
¯ =≥, ≤
ISi = {¬Qo p (s)} −→ Si+1 = ISi ∪{Qō p (s)} (Note that: < ¯ =>, > ¯ =<)
¯ =≤, ≥
Rule Ru :
ISi = {(Q1◦p u Q2◦q )(s)} −→ ISi+1 = ISi ∪ {Q1◦p (s), Q2◦q (s)}
Rule Rt :
0 00
ISi = {(Q1◦p t Q2◦q )(s)} −→ ISi+1 = ISi ∪ {Q1◦p (s)} , ISi+1 = ISi ∪ {Q2◦q (s)}
Rule Rv :
ISi = {(Q1◦p v Q2◦q ), Q1◦p (s)} −→ ISi+1 = ISi ∪ {Q2◦q (s)}
0
Rule Rv :
ISi = {(Q1≥p v Q2◦q ), Q1=r (s), p ≤ r} −→ ISi+1 = ISi ∪ {Q2◦q (s)}
Rule Rreduction :
ISi = {Q≥p (s), Q≤p (s)} −→ ISi+1 = ISi − {Q≥p (s), Q≤p (s)} + {Q=p (s)}
ISi = {Q≥p (s), Q≥q (s), p ≥ q} −→ ISi+1 = ISi − {Q≥q (s)}
ISi = {Q≥p (s), Q=q (s), q ≥ p} −→ ISi+1 = ISi − {Q≥p (s)}
ISi = {Q≤p (s), Q≤q (s), p ≤ q} −→ ISi+1 = ISi − {Q≤q (s)}
ISi = {Q≤p (s), Q=q (s), p ≥ q} −→ ISi+1 = ISi − {Q≤p (s)}
Fig. 2. A sample of the propagation rules
5.2 An illustrative example
We illustrate the use of the proposed algorithm to decide whether the dymanic
QoS of services S1 and S2 is acceptable with respect to the following required
QoS:
RQ = (GoodRC≥0.4 u ¬BadT R≥0.8 ) t GoodRC≥0.9 .
Acceptance of the QoS of S2 The QoS of the service S2 is acceptable w.r.t
RQ if:
hT Boxexple , ABoxexple i |= RQ(S2 ).
That is, hT Boxexple , ABoxexple ∪ {¬RQ(S2 )}i is inconsistent
where,
T Boxexple = {BadRC1 v GoodRC0 }
ABoxexple = {GoodT R1 (S2 ), BadRC1 (S2 )}
The reasoning algorithm generates inference systems ISi by applying the
propagation rules as follows:
IS0 = {T Boxexple , ABoxexple , ¬RQ(S2 )}
= {BadRC1 v GoodRC0 , GoodT R1 (S2 ), BadRC1 (S2 ), ¬[(GoodRC≥0.4 u
¬BadT R≥0.8 ) t GoodRC≥0.9 ](S2 )}
IS1 = IS0 ∪{GoodRC0 (S2 ), [¬(GoodRC≥0.4 u ¬BadT R≥0.8 ) u ¬GoodRC≥0.9 ] (S2 )}
(generated by Rv and pushing the negation)
IS2 = IS1 ∪ {¬(GoodRC≥0.4 u ¬BadT R≥0.8 )(S2 ),
¬GoodRC≥0.9 (S2 )} (generated by Ru )
IS3 = IS2 ∪ {(GoodRC<0.4 t BadT R≥0.8 )(S2 ), GoodRC<0.9 (S2 )} (generated
by R¬ )
IS40 = IS2 ∪{(GoodRC<0.4 (S2 ), GoodRC<0.9 (S2 )} or IS400 = IS2 ∪{(BadT R≥0.8 )(S2 ),
GoodRC<0.9 (S2 )} ( IS40 , IS400 generated by Rt )
IS50 = IS40 ∪ {(GoodRC<0.4 (S2 )} (generated by Rreduction on IS40 )
IS60 = IS50 ∪ {(GoodRC0 (S2 )} (generated by Rreduction with GoodRC0 (S2 ))
The algorithm stops at the inference system IS60 because no more rule can
be applied.
As there is no clash in IS60 , the system is not inconsistent (without performing
the inference system IS400 ). That is, it exists an interpretation such that:
hT Boxexple , ABoxexple i |= ¬RQ(S2 )
We can deduce that the service S2 is not acceptable w.r.t. the required QoS RQ.
Acceptance of the QoS of S1 The QoS of the service S1 is acceptable w.r.t
RQ if:
hT Boxexple , ABoxexple i |= RQ(S1 ).
That is,
hT Boxexple , ABoxexple ∪ {¬RQ(S1 )}i is inconsistant
where,
T Boxexple = {Φ}
ABoxexple = {BadT R0.25 (S1 ), GoodT R0.75 (S1 ), GoodRC0.5 (S1 ), BadRC0.5 (S1 )}
The reasoning algorithm generates inference systems ISi by applying the
propagation rules as follows:
IS0 = {T Boxexple , ABoxexple , ¬RQ(S1 )}
= {BadT R0.25 (S1 ), GoodT R0.75 (S1 ), GoodRC0.5 (S1 ), BadRC0.5 (S1 ),
¬[(GoodRC≥0.4 u ¬BadT R≥0.8 ) t GoodRC≥0.9 ](S1 )}
IS1 = IS0 ∪{GoodRC0 (S2 ), [¬(GoodRC≥0.4 u ¬BadT R≥0.8 ) u ¬GoodRC≥0.9 ] (S1 )}
(generated by pushing the negation)
IS2 = IS1 ∪ {¬(GoodRC≥0.4 u ¬BadT R≥0.8 )(S1 ), ¬GoodRC≥0.9 (S1 )} (gen-
erated by Ru )
IS3 = IS2 ∪{(GoodRC<0.4 t BadT R≥0.8 )(S1 ), GoodRC<0.9 (S1 )} (generated
by R¬ )
IS40 = IS3 ∪{GoodRC<0.4 (S1 ), GoodRC<0.9 (S1 )} or IS400 = IS3 ∪{BadT R≥0.8 (S1 ),
GoodRC<0.9 (S1 )} ( IS40 , IS400 are generated by Rt )
IS400 contains the clash {BadT R≥0.8 (S1 ), BadT R0.25 (S1 )}
IS50 = IS40 ∪ {(GoodRC<0.4 (S1 )} (generated by Rreduction on IS40 )
IS50 contains the clash : GoodRC<0.4 (S1 ), GoodRC0.5 (S1 )
The algorithm stops because all the inference systems contain a clash. There-
fore, the system is inconsistent.
This means that the service S1 is acceptable w.r.t. the required QoS RQ.
6 Conclusion and Future Work
In this paper, we proposed a high level support for describing the semantic
QoS variations in order to react to the service behavior changes. It provides an
appropriate adaptation and avoid the process to grind to a halt. The key element
of our model is the probabilistic extension of DL proposed. This extension allows
to express the QoS requirements and performs an efficient reasoning mechanism
to let the system self healing. Our variant of description logic has a minimal
set of logical constructors. It contains only concept terms and two constructors
which are conjunction and disjunction. As future work, we plan to extend the
proposed approach by providing more constructors.
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