Evolutionary strategies for ultra-large-scale autonomic systems

Ultra-large-scale (ULS) systems originate from the need to address social problems that are getting more and more complex, such as climatic monitoring, transportation, citizens protection and security. These factors imply a continuous increase of information systems, requiring digital communication networks allowing for communication between people, between machines, and machines and people. The aim of this paper is to present a novel approach for the design of highly adaptive ULS systems, with the focus on computer-supported evolution, adaptable structures, emergent behaviors as well as advanced monitoring and control techniques. We illustrate the Networked Autonomic Machine (NAM), a framework for the characterization of self-managing, highly reconfigurable ULS systems, and the Adaptive Evolutionary Framework (AEF), for the implementation of distributed evolutionary strategies. Finally, we show their effectiveness in the design of an ULS overlay network provided with evolutionary strategies for facing denial-of-service attacks.