F. Akkawi, A. Bader, and T. Elrad (USA)
Aspect-Oriented Software Development, IntelligentAgents, Reusability, Machine Learning.
Concurrent real-time applications are vulnerable to performance and reliability concerns due to environmental changes. Building intelligent concurrent applications that are able to adapt to environmental changes and to be able to reconfigure them selves is the key factor in avoiding performance degradation of concurrent real-time software applications.. In this paper we present the Dynamic Weaver Framework (DWF) that was designed and developed using aspect-oriented software development, that uses a machine learning-based approach to addresses the design of agent-based intelligent concurrent software applications in order to ensure the reliability and performance properties for such an application. Although reliability and performance are conflicting requirements in most of the cases, we will show how to use an aspect-oriented technology by which these requirements can be designed, implemented, reused, and replaced in isolation from each other. The performance and reliability of the software system can be reasoned about by intelligent agents who can direct the system to reconfigure itself in order to adapt to the environment changes. The agents rely on the data-mining techniques to discover patterns of performance degradation or imminent signals of reliability violation and to predicate policies that cope best with the environmental fluctuations.
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