Continuous reasoning for adaptive container image distribution in the cloud-edge continuum
Damiano Azzolini, Stefano Forti, Antonio Ielo
TL;DR
This paper tackles adaptive distribution of container images across a cloud-edge continuum, where image start-up delays threaten performance. It introduces a declarative pipeline that uses Answer Set Programming to compute cost-optimal initial placements and Prolog-based continuous reasoning for runtime adaptation, implemented in the open-source tool declace. Through simulations, the authors show that continuous reasoning speeds up runtime decisions and limits migrations, achieving a practical balance between cost efficiency and prompt responsiveness in dynamic, heterogeneous infrastructures. The approach advances container image distribution by enabling continuous, QoS- and context-aware adaptation, with potential impact on edge intelligence and fast-reacting cloud-edge applications.
Abstract
Cloud-edge computing requires applications to operate across diverse infrastructures, often triggered by cyber-physical events. Containers offer a lightweight deployment option but pulling images from central repositories can cause delays. This article presents a novel declarative approach and open-source prototype for replicating container images across the cloud-edge continuum. Considering resource availability, network QoS, and storage costs, we leverage logic programming to (i) determine optimal initial placements via Answer Set Programming (ASP) and (ii) adapt placements using Prolog-based continuous reasoning. We evaluate our solution through simulations, showcasing how combining ASP and Prolog continuous reasoning can balance cost optimisation and prompt decision-making in placement adaptation at increasing infrastructure sizes.