A survey of evolutionary and embryogenic approaches to autonomic networking

The term ''autonomic networking'' refers to network-level software systems capable of self-management, according to the principles outlined by the Autonomic Computing initiative. Autonomicity is widely recognized as a crucial property to harness the growing complexity of current networked systems. In this paper, we present a review of state-of-the-art techniques for the automated creation and evolution of software, with application to network-level functionalities. The main focus of the survey are biologically-inspired bottom-up approaches, in which complexity is grown from interactions among simpler units. First, we review evolutionary computation, highlighting aspects that apply to the automatic optimization of computer programs in online, dynamic environments. Then, we review chemical computing, discussing its suitability as execution model for autonomic software undergoing self-optimization by code rewriting. Last, we survey approaches inspired by embryology, in which artificial entities undergo a developmental process. The overview is completed by an outlook into the major technical challenges for the application of the surveyed techniques to autonomic systems.