Continuous reasoning for adaptive container image distribution in the cloud-edge continuum
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912309938487296 |
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| author | Azzolini, Damiano Forti, Stefano Ielo, Antonio |
| author_facet | Azzolini, Damiano Forti, Stefano Ielo, Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12605 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continuous reasoning for adaptive container image distribution in the cloud-edge continuum Azzolini, Damiano Forti, Stefano Ielo, Antonio Distributed, Parallel, and Cluster Computing Artificial Intelligence Logic in Computer Science Software Engineering 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. |
| title | Continuous reasoning for adaptive container image distribution in the cloud-edge continuum |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Logic in Computer Science Software Engineering |
| url | https://arxiv.org/abs/2407.12605 |