Abstract
This paper shows how self-$^{ast}$ mechanisms give rise to complex but predictable and therefore steerable global system behavior in a cooperative computing environment. The operation of and the interactions between a set of networked autonomic devices are simulated. These are used as access points to a number of services, have the ability to accept or delegate execution of the associated tasks, and can adjust their internal state in response to the demand. At initialization, all devices are assigned a random internal state, i.e., there is no correlation between their configuration and the tasks that they are expected to perform. The authors study the emergence of cooperation and find that it spontaneously occurs when specific conditions are met, which allow individual devices to focus on performing a single task, sacrificing their ability to efficiently perform others. A relatively simple model that can be completely and thoroughly analyzed was chosen so as to demonstrate how the methodology developed to study complex adaptive systems in biology can be a powerful tool when planning the deployment of large ensembles of interacting autonomic devices.