FUZZY LOGIC BASED QUALITY OF SERVICE MODELS Jo˜ 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 Copyright c  2011 SCITEPRESS (Science and Technology Publications, Lda.)