FUZZY LOGIC BASED QUALITY OF SERVICE MODELS

ao Antunes, Andr

e Vasconcelos and Jos

e Tribolet

INESC Inovac¸

ao, Instituto de Engenharia de Sistemas e Computadores and

Instituto Superior T

ecnico, Technical University of Lisbon, Lisbon, Portugal

Keywords:

Fuzzy logic, Quality of service models, Complex information systems, CEO framework.

Abstract:

The continuous monitoring of information systems’ quality of service increases importance as business be-

comes more and more dependent of those systems. In order to obtain that view, quality models need to be

deﬁned for those systems. Because of its complexity and today modelling frameworks, quality models tend

to result in a poor representation of reality, mainly because of their lack of ability to represent uncertainty. In

this work, we investigate the use of fuzzy logic’s properties to create a new kind of quality of service models,

which handles uncertainty and imprecision naturally. The objective is to obtain models that are a better repre-

sentation of reality and easier to create and understand. This article presents the investigation on related topics

to support the identiﬁed problem and motivations, followed by a solution proposal and a validation scenario.

1 INTRODUCTION

Along the years, several quality models have already

been proposed, both for the implementation of the in-

formation systems as for the evaluation of its general

performance (S.O. et al., 2010). Their main goal is

to ﬁnd the answer to the question ”What is quality?”.

This answer is commonly built by deﬁning what char-

acteristics are considered important to exist in a ser-

vice (ISO, 2001).

In the case of evaluation of information systems’

performance, there are multiple dimensions that can

be quantiﬁed, and from those the deﬁnition for qual-

ity’s characteristics can be created. By performance

we mean how well a system, already assumed correct,

works (Khaddaj et al., 2004).

As systems increase in complexity, so do the rules

that reason about its state, or in other words, the

quality of service model becomes more complex and

harder to understand. In addition, the quantitative val-

ues, obtain from monitoring speciﬁc system’s dimen-

sions, are usually modeled using boolean like logics

where, for instance, abrupt variations in quality level

can occur, consequence of strict thresholds have to be

deﬁned (Zadeh, 1973).

In such complex information systems, concluding

on the quality level carries much uncertainty and by

forcing ﬁxed thresholds to separate different levels,

the model will no longer represent the reality, where

for some range of values the conclusion about the cor-

respondent level of quality can be fuzzy (Campbell

et al., 1996).

So, the challenge presented in this scenario is to

create models that give a more accurate view of reality

and at the same time require less effort to create and

understand.

1.1 Motivation

Human reasoning is known for its ability to process

incomplete and vague information in order to infer re-

sults or make decisions. As said in the previous sec-

tion, that property presents serious challenges when

creating a model to represent it. That is especially

true if logics that work with scales measured by dis-

crete values are used. A alternative to such logics is

Fuzzy logic (Zadeh, 1973).

By combining the paradigm of the if-then rules

with the descriptive capacity of the linguistic vari-

ables, more comprehensive models can arise, while

at the same time reducing the effort of their develop-

ment and improving its ﬁnal quality.

1.2 Objectives

The main goal of this investigation is then to evalu-

ate how fuzzy logic’s concepts can improve the con-

struction of an answer to the question ”what is qual-

ity?”. Our objective is also to improve that process, so

a more natural transition from human reasoning real-

516

Antunes J., Vasconcelos A. and Tribolet J..

FUZZY LOGIC BASED QUALITY OF SERVICE MODELS.

DOI: 10.5220/0003694305160519

In Proceedings of the International Conference on Evolutionary Computation Theory and Applications (FCTA-2011), pages 516-519

ISBN: 978-989-8425-83-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

ity to a quality of service model, can be achieved.

In order to build such models we’ll need some

framework to provide us with a common set of con-

cepts to describe all the parts and their relations. Be-

ing this investigation under the scope of the Center

for Organizational Design and Engineering (CODE)

the preferred framework for this task will be the CEO

(Vasconcelos et al., 2007). Because CEO framework

does not consider fuzzy logic and some quality of ser-

vice related concepts, an extension proposal will be

presented in order to enable the creation of the mod-

els required to this investigation.

2 RELATED WORK

To have models, we perform modeling. According

to (Silva and Videira, 2001) modeling is both an art

and a science, and a model is the interpretation of a

subset from the real world. Through the simpliﬁca-

tion of the reality down to a set of concepts and rela-

tions, different languages can be used to describe the

model according to the audience expectations, knowl-

edge and/or objectives.

2.1 Quality of Service

Quality is generally deﬁned as a multidimensional

concept (Khaddaj et al., 2004). These dimensions are

used to construct a quality model, as also described

in the International Standard ISO 9126. As for ser-

vice, in the domain of enterprise architectures, (Open,

2006) deﬁnes it as a mechanism that provides access

to capabilities. In the ﬁeld of networking, the notion

of quality of service is associated with the guaran-

tees of performance transporting a ﬂow of informa-

tion, measured mostly through speciﬁc metrics.

A broad review of quality of service investigation

is presented in (Campbell et al., 1996), which de-

fends that investigation is mostly focused on individ-

ual layers, instead of addressing the overall QoS Ar-

chitecture. Also, a generalized QoS framework is de-

scribed, to include principles about its construction,

speciﬁcation and mechanism to handle the system’s

behaviour. QoS speciﬁcation is described as the cap-

turing of quality level requirements and management

policies, which will be different for each architectural

layer. As for QoS Mechanisms, it will vary accord-

ing to the speciﬁcation and can be separated in three

groups: provision, control and management mecha-

nisms.

On the topic of QoS speciﬁcation, (S.O. et al.,

2010) presents a survey of models, according to a

classiﬁcation set. The authors conclude that perfor-

mance is itself subjective and open to different in-

terpretations. Also, most models were found to be

limited by their inability of handle uncertainty and

imprecision. Some approaches, like the probabilistic

models, which can handle vagueness, is however not

adequate to handle information expressed in natural

language.

2.2 Enterprise Architectures

Based on (Lankhorst, 2005) to manage the complex-

ity of large systems it’s necessary to have an archi-

tecture, which captures all the components, their re-

lationships with each other and their surrounding en-

vironment. An architecture also provides a common

language to describe the system, it’s components and

their relations, improving overall communication be-

tween the stakeholders.

This notion was extended to the ﬁeld of enterprise

engineering and from that originated the term of En-

terprise Architecture (EA). An enterprise is seen as

a complex system where through a ”whole of princi-

ples, methods and models” it can be decomposed in

individual functional parts with respective relations.

2.2.1 Framework CEO

CEO framework (Vasconcelos et al., 2007) originated

in CODE investigation group, and its goal was set to

describe organizational knowledge as its various lev-

els and the dependency between them. CEO decom-

poses an organization on three separate levels: organi-

zational goals, business processes and resources, each

with adequate forms of representation according to

the concepts in question.

CEO uses the Uniﬁed Modeling Language (UML)

for implementation, with the help of developed

stereotypes. In order to represent information sys-

tems’ concepts, the framework extended its meta

model with the necessary concepts, which will be the

starting point for our own extension proposal.

3 SOLUTION PROPOSAL

Our solution is targeted at reducing some of the com-

plexity in models by creating models that don’t force

the removal of uncertainty. That will be achieved

through the use of fuzzy logic’s concepts, using the

method described in the following section.

FUZZY LOGIC BASED QUALITY OF SERVICE MODELS

517

3.1 Fuzzy Models

The fuzzy controller, as the most successful applica-

tion of fuzzy logic, will be used to guide the develop-

ment of the new quality of service models. So, the

main focus for the new models will be in the cre-

ation of the knowledge database. In order to do so,

the necessary variables need to be deﬁned, that in this

case correspond to all system’s measurable dimen-

sions and quality factors.

Regarding the if-then rules, we’ll consider as input

variables those corresponding to measurable dimen-

sions, and as output variables the quality factors. In

order to obtain the ﬁnal and crisp results for the eval-

uated rules, its also important that the chosen method

to the defuzziﬁcation is not too complex to avoid mak-

ing the models hard to understand and/or create (e.g.

Centre of Gravity).

As for the all necessary fuzzy reasoning knowl-

edge, we’ll follow the original work done in (Zadeh,

1973).

3.2 Metamodel Extension

Regarding the extension to CEO framework, after de-

scribing the main characteristics of the new QoS mod-

els, the concepts to be proposed are the following:

• Measurable Dimension. Any system dimension

that can be observed and quantiﬁed. This concept

is connected with ”IT Block”, from CEO Frame-

work.

• Quality Factor. Relevant characteristic for the

deﬁnition of quality in a system. This concept is

connected with ”IT Service”.

• Linguistic Value. Represents a value of a lin-

guistic variable. Each measurable dimension and

quality factor will be composed by these values.

By connecting two different values, from a mea-

surable dimension and a quality factor, we’ll be

deﬁning a if-then rule.

• Fuzzy Operator. This represents an operator to

compose more complex rules.

Using the concepts present previously, the new

models can be represented in order to document or

share between stakeholders. A visual representation

of the extension proposed to the meta model can be

seen in ﬁgure 1. As different stakeholders may have

different perspectives over a model, we also propose

a set of views, each with unique objectives:

• System Quality View. The ﬁrst view is an high

level view of the model, where for a speciﬁc sys-

tem and services, only the measured dimensions

and considered quality factors are present.

Figure 1: Proposed extension to CEO meta model.

• Quality Factor View. This view focus on a spe-

ciﬁc service’s quality factor, relating it to all di-

mensions that cause some impact, and also the

values involved.

• Measured Dimension View. In this view the fo-

cus is on a system’s dimension, showing all qual-

ity factors that are inﬂuenced by it, as for respec-

tive values.

4 SOLUTION VALIDATION

In order to validate the proposed solution, we’ll use

it to develop a quality of service model for an ac-

tual information system, inside a real company, Por-

tugal Telecom Comunica

oes (PT-C). This model will

be used in the continuous and real-time monitoring of

such system, which is performed by the Pulso Plat-

form.

PT-C created the Pulso platform (Alegria et al.,

2005) because of a growing concern on its informa-

tion systems’ conditions. One of its goals was to mon-

itor performance, availability and errors of their IT

assets with near real-time precision, to determine the

quality of service being provided to users. To achieve

that a network of agents, attached to relevant system’s

components, provides basic metrics that correlate and

aggregate into indicators.

The solution presented previously will be applied

to obtain the necessary quality of services indicators.

The current version of Pulso platform, speciﬁcally the

models used today, will serve as basis for comparison

with the developed fuzzy models, in order to deter-

mine the gains and/or limitations imposed by this so-

lution.

4.1 Preliminary Test

As a preliminary test for the creation of the new qual-

ity of service model, this extension of CEO’s meta

model was applied to a ﬁctional example. In this ex-

ample we considered an ”IT Block” representing a

FCTA 2011 - International Conference on Fuzzy Computation Theory and Applications

518

server, named ”Server X”. As for the service it pro-

vides we consider it as ”Infrastructure Support”. As

for measurable dimensions from the server, ”CPU Us-

age” and ”Memory Usage” are available, and for the

service’s quality factors, ”Availability” is to be evalu-

ated. All those variables are composed by two values,

”Low” and ”High”. So, the knowledge base for this

example will be the following:

• IF CPU Usage IS Low AND Memory Usage IS Low

THEN Availability IS High

• IF CPU Usage IS High OR Memory Usage IS High

THEN Availability IS Low

This model can be viewed in ﬁgure 2 which uses

the new concepts proposed in the meta model exten-

sion.

Figure 2: Preliminary test with meta model extension.

5 CONCLUSIONS

From research done so far, we are conﬁdent to say

that including the tools to handle imprecision and un-

certainty is a vital step to achieve better quality of ser-

vice models. By having models that better represent

the reality, we also expect to see great improvement in

at least two properties: the effort required to develop

models and there complexity should decrease by con-

sequence. In companies where many complex infor-

mation systems exist, these improvements can have

signiﬁcant impact. In this article we proposed a solu-

tion based on fuzzy concepts, which provide us with

a formal method to handle uncertainty. From a set

of quantiﬁed dimensions, quality factors can now be

deﬁned by correlation rules, even enabling the mod-

eling of very speciﬁc cases. Also it enables an easier

extension of the knowledge that deﬁnes the quality of

service. An extension for the CEO Framework’s meta

model also is part of the proposed solution. This ex-

tension permits a visual representation of the deﬁned

rules, and also to establish the link between the pre-

vious concepts in the framework. Three new views,

related to quality of service, are also proposed to ad-

dress different perspectives. Finally, the application

of the solution to a ﬁctional system, gives an idea of

future application to validate this investigation.

REFERENCES

Alegria, J., Carvalho, T., and Ramalho, R. (2005). Uma

experi

encia open source para “tomar o pulso” e “ter

pulso” sobre a func¸

ao sistemas e tecnologias de

informac¸

ao. Technical report, Portugal Telecom.

Campbell, A., Campbell, A., Aurrecoechea, C., and Hauw,

L. (1996). A review of qos architectures. multimedia

systems, 6:138–151.

ISO (2001). ISO/IEC 9126-1:2001, Software engineering –

Product quality – Part 1: Quality model.

Khaddaj, S., Khaddaj, S., and Horgan, G. (2004). The eval-

uation of software quality factors in very large infor-

mation systems. Electronic Journal of Information

Systems Evaluation, pages 43–48.

Lankhorst, M. (2005). Enterprise Architecture at Work:

Modelling, Communication and Analysis. Springer.

Open, O. (2006). Reference model for service oriented ar-

chitecture 1.0. Technical report, OASIS Open.

Silva, A. and Videira, C. (2001). UML - Metodologias e

Ferramentas Case. Centro Atl

antico.

S. O., O., E. O, O., F. M. E, U., Mbarika, V., and B. A,

A. (2010). A survey of performance evaluation mod-

els for distributed software system architecture. Pro-

ceedings of the World Congress on Engineering and

Computer Science 2010, 1:35–43.

Vasconcelos, A., Sousa, P., and Tribolet, J. (2007). Infor-

mation system architecture metrics: An enterprise en-

gineering evaluation approach. Electronic Journal of

Information Systems Evaluation, 10(1):91–122.

Zadeh, L. (1973). Outline of a new approach to the analy-

sis of complex systems and decision processes. IEEE

Trans. on Systems, Man, and Cybernetics, SMC-3:28–

44.

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