Guiding Complex Design Optimisation Using Bayesian Networks

In this chapter we aim to present an approach to addressing the problem of architectural optimisation of complex computer-based systems. Initially, we provide a survey of methods created with the direct aim of optimising systemwith respect to their non-functional qualities. An overview of commonalities and differences between these methods leads to exploration of reasons for their success in attaining their respective goals. The results of this discussion provide the information necessary to formulate the definition of the notion of guidance in the context of architectural design and optimisation. Following that, we describe a newHeuristic-based Architectural Optimisation framework developed in an attempt to unify the lessons learnt from the successes and shortcomings of the surveyed optimisation approaches. Special attention is paid to the guidance mechanism employed by the new framework to control the progress of design optimisation. We propose to develop a suitable guidance mechanism using a Bayesian Belief Network (BBN) obtained from a combination of hybrid simulation modeling and BBN discovery algorithm. Finally, the rest of the chapter is devoted to a description of a brief example problem. There, an attempt is made to show how the guidance mechanism can be applied to a fairly simple problem that nonetheless possesses an element of ambiguity that so often falters system design optimisation activities.