Cohabitation of Intelligence and Systems: Towards Self-reference in Digital Anatomies

Abstract

The modern computing scenario of the Computing Continuum exhibits large and complex applications with heterogeneous requirements running on distributed infrastructure. Still, when it comes to coordinating and controlling such applications and infrastructures, it is common to rely on centralized or ad-hoc solutions. While these approaches are robust, scaling management solutions, managing local changes, and having a holistic perspective can be challenging. Additionally, they could be better suited for addressing new problems in dynamic environments. Therefore, new approaches are needed. In this paper, we present Dict, a novel method for managing the Computing Continuum, i.e., the infrastructure and the applications. The proposed approach encompasses a series of modules for automatic management. The core idea is to develop a method for applying the intents coming from the infrastructure and application managers in an autonomic and dynamic way. The modules can communicate through coordinators that take observable inputs and send them back predictions on the next actions to take. These coordinators have the role of summarizing the sensed observation and extracting high-level information in light of the AI advancement that shows how discrete space representation of inputs improves generalization. Thus, they can have models that build their own semantics and “language.” We envision that, through Dict, both the application and the infrastructure management will only have to specify high-level intents and not focus on defining encoded and difficult-to-change strategies.