A Study on the Methods for Performance Design and Improvement in Collaborative Systems(AI Applications)(<Special Issue>Doctorial Theses on Aritifical Intelligence)
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概要
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Conventionally, system performance Is evaluated and performance improvement is carried out much after the implementation of the system This is an extremely difficult, labonous and costly task. The aim of this research is to project system performance estimate at the requirement analysis and design phase itself, much before the implementation phase. This study introduces novel tools for modelling the system, for evaluating its performance and for improving and enhancing its performance The target systems chosen are collaborative systems. Three distinct scenarios of collaborative activity are being examined in detail At the centre of each scenario is a server model that seeks to represent a particular type of service in collaborative systems Single and parallel servers model the general type of collaborative activity Composite servers model the composite form of collaborative activity, while distributed servers represent the distributed type of collaborative activity The discrete-event and client-server properties of the collaborative systems are exploited in modelling, performance evaluation simulation and performance improvement strategy Modelling is done by the descriptive "Multi-Context Map" (MCM) technique. System evaluation is through GPSS simulation and improvement is by the Expert System reasoning with qualitative rules MCM captures the workflow in a collaborative system wherem collaborators interact with each other through the exchange of Token, Material and Information (TMI) This triple-input-triple-output is what distinguishes contexts in MCM from ordinary single-input-single -output servers in queueing networks These three additional interactions present a formidable challenge to improve the system quantitatively To overcome the computational complexity, Qualitative Reasoning (QR) disciphne of AI is used in constructing the knowledge-base of the Expert System The integrated environment of modelling, performance evaluation and performance improvement is semi-automatic by design, and as demonstrated by the successful application to the performance design and improvement of the benchmarking systems, can be extended to real life collaborative systems
- 2005-01-01