Continuous reasoning for adaptive container image distribution in the cloud-edge continuum

TL;DR

This paper introduces a declarative, logic programming-based approach for adaptive container image distribution across cloud-edge environments, optimizing placement considering resources, network QoS, and costs.

Contribution

It presents a novel combination of Answer Set Programming and Prolog for initial placement and continuous adaptation of container images in cloud-edge systems.

Findings

Effective balancing of cost and responsiveness demonstrated in simulations

Combines ASP and Prolog for initial placement and ongoing adaptation

Scalable solution for diverse cloud-edge infrastructures

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.