Abstract — One of the biggest challenges in obtaining truly self-managed networks is to automate the process of software evolution, and in particular, the evolution of protocol implementations and configurations. In this paper we explore an approach to network evolution that works inside the network software to manage and which operates directly at the code level. We investigate related code steering techniques in two directions: One is the fully automatic selection of protocol service elements where, depending on device characteristics and current operation environment, each communication entity has to select among a potentially wide variety of protocol implementations providing similar services. The other direction relates to the automatic synthesis of new protocol elements which are the result of optimizing existing implementations for a specific context. We use genetic programming as a tool to generate new configurations and new code automatically. In this paper we present a framework for injecting such code into a running environment in a nondisruptive way and report on first exploratory results on resilient protocol evolution. I.

Abstract. We explore a homeostatic approach to program execution in computer systems: the “concentration ” of computation services is regulated according to their fitness. The goal is to obtain a self-healing effect so that the system can resist harmful mutations that could happen during on-line evolution. We present a model in which alternative program variants are stored in a repository representing the organism’s “genotype”. Positive feedback signals allow code in the repository to be expressed (in analogy to gene expression in biology), meaning that it is injected into a reaction vessel (execution environment) where it is executed and evaluated. Since execution is equivalent to a chemical reaction, the program is consumed in the process, therefore needs more feedback in order to be re-expressed. This leads to services that constantly regulate themselves to a stable condition given by the fitness feedback received from the users or the environment. We present initial experiments using this model, implemented using a chemical computing language. 1

The complexity of distributed computing systems and their increasing interaction with the physical world impose challenging requirements in terms of adaptation, robustness, and resilience to attack. Given the ability of natural organisms to respond to adversity, many researchers have investigated biologically inspired approaches to designing robust distributed systems. Examples include biomimetics, which mimic behaviors such as swarming found in nature, as well as evolutionary computation methods, such as genetic algorithms and artificial neural networks, which simulate the natural processes that produce those behaviors. A related but fundamentally different technique is digital evolution, whereby a population of self-replicating computer programs exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. Over thousands of generations, these organisms can evolve to survive, and thrive, under extremely dynamic and adverse conditions. In this paper, we describe a study in the use of digital evolution to produce distributed cooperative behavior, specifically leader election, in a population of digital organisms. Our results demonstrate that digital evolution can produce organisms capable of electing a leader and, when that leader is terminated, electing a new leader. These digital organisms have no “built-in” ability to perform this task; each population begins with a single organism