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

arXiv: https://arxiv.org/abs/2407.12605 · HTML: https://mlsanity.com/paper/2407.12605/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 17 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions](https://mlsanity.com/paper/1807.00976/) (2018)
- [EdgePier: P2P-based Container Image Distribution in Edge Computing Environments](https://mlsanity.com/paper/2109.12983/) (2021)
- [Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions](https://mlsanity.com/paper/2005.10460/) (2020)
- [Probabilistic QoS-aware Placement of VNF chains at the Edge](https://mlsanity.com/paper/1906.00197/) (2019)

## Abstract (arXiv)

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.