Complex Application Architecture Dynamic Reconfiguration Based on Multi-criteria Decision Making

Intelligent Transportation Systems (ITS) are increasingly important since they aim to bring solutions to crucial problems related to transportation networks such as congestion and various road incidents. Management of ITS, as other complex and distributed applications, has to cope with unforeseeable events and incomplete data while guaranteeing a quality of service (QoS) defined by multiple criteria reflecting real-life needs. To enable applications to adapt to changing environments, we define a methodology of dynamic architecture reconfiguration based on multi-criteria decision making (MCDM) using evolutionary computing (EC) to find the best combination of architecture components. We use the Pareto Evolutionary Algorithm Adapting the Penalty (PEAP), a category of EC, selected in this paper to deal with timeconsuming online processing required by basic EC such as genetic algorithms. Our simulation results relating to road safety highlight the benefits of MCDM prior to such reconfiguration. We also address the problem of destabilization which can result from repeated reconfigurations in response to ongoing environment changes.