Architectural optimisation guidance of complex-based systems

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NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Recent years have seen an increase in the number of application domains taking advantage of computer-based systems. With the ubiquity of use for these types of systems came an associated increase in their complexity, both in terms of compositional features and interactions with the operational environment. As a result, in many situations the non-functional qualities of systems play a major role in the success of the endeavours these systems are built to support. Consequently, the system designers need to engage in optimisation, which involves exploration of options for creation of candidate designs that better match the goals and constraints imposed by the system environment. To be successful, the designer must have a good understanding of factors and criteria that influence the system qualities and be able to manage the trade-offs and entanglements which may arise when multiple non-functional qualities are linked to a single architectural feature or design decision. The research presented in this thesis draws upon existing research to formulate a framework capable of supporting this type of optimisation and then extends it by proposing a mechanism for guiding the designer in this activity. To this end, the method aims to produce a description of the possible causal relationships between the compositional characteristics of a given system and the levels of qualities it attains. The mechanism relies on principles of multi-paradigm simulation to generate a set of data which is then processed by an algorithm to extract a Bayesian Belief Network representation of causalities present in the source system. It is recognised that the advantages associated with obtaining causal information using the proposed guidance method could be undone if the process of its application in realistic settings is too complicated and time consuming. With this in mind, a design study was conducted to assess the practicality of applying the steps necessary to obtain guidance information in the context of a real design problem. The results of this investigation has shown that the method can produce a depiction of causalities useful in identification of design change options most likely to advance the non-functional qualities of the system.
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