Modeling spaces for real-time embedded systems

No system in the real world can compute an appropriate response in reaction to every situation it encounters, or even most situations it is likely to encounter. Biological systems address this issue with four strategies: (1) a repertoire of already computed responses tied to a situation recognition process, (2) organized in a response-time hierarchy that allows a quick response to occur immediately, and one or more slower and more deliberate responses to begin at the same time, with (3) decision processes that allow one of them to take over after a little while, or that (4) merge several of them in a combined and possibly novel response. In this paper, we describe an approach to building self-adaptive computing systems that incorporates these strategies, to cope with their intended use in hazardous, remote, unknown, or otherwise difficult environments, in which it is known a priori that the system cannot keep up with all important events, and that “as fast as possible” is not appropriate for some interactions. The key to implementing these strategies is an abstraction/refinement hierarchy of behavioral models and processes at multiple levels of granularity and precision. The key to coordinating these different models is the collection of integrative mappings among them, which are developed along with the models, and used for managing system behavior. We also describe the system development process that we use to build such systems, which differs from conventional methods by taking the basic artifacts of development, considered as partial models of aspects of the system in its environment, and retains them all in a model hierarchy, which eventually becomes the definition of the run time system. We show how to implement such systems, explain why we think they are good candidates for real-time operational environments, and illustrate the method with an example implementation.