Natural engineering: applying a genetic computing model to engineering self-aware software

Current approaches to software engineering use a Turing machine implementation to intelligently monitor and adjust the internal environment of an algorithm in real-time. These same approaches however, fail to account for fluctuations in the external environment of the computation, leading to a gross underutilization of system resources or requiring a full restart with costly supervision and manual intervention. In this paper, we describe how we can provide the same intelligence for non-functional requirements as there exist for functional requirements in software applications by using the Distributed Intelligence Computing Element (DIME) computing model. By discussing this model in comparison to similar systems in nature, namely in the context of genetics, we develop the concept of services engineering with self-managed software. As a particularly salient example of this model in practice, we explore the potential for such an approach to improve the performance of machine and deep learning algorithms as a function of intelligent computing environments.