{"ID":2893094,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13895","arxiv_id":"2507.13895","title":"Application Placement with Constraint Relaxation","abstract":"Novel utility computing paradigms rely upon the deployment of multi-service applications to pervasive and highly distributed cloud-edge infrastructure resources. Deciding onto which computational nodes to place services in cloud-edge networks, as per their functional and non-functional constraints, can be formulated as a combinatorial optimisation problem. Most existing solutions in this space are not able to deal with \\emph{unsatisfiable} problem instances, nor preferences, i.e. requirements that DevOps may agree to relax to obtain a solution. In this article, we exploit Answer Set Programming optimisation capabilities to tackle this problem. Experimental results in simulated settings show that our approach is effective on lifelike networks and applications.","short_abstract":"Novel utility computing paradigms rely upon the deployment of multi-service applications to pervasive and highly distributed cloud-edge infrastructure resources. Deciding onto which computational nodes to place services in cloud-edge networks, as per their functional and non-functional constraints, can be formulated as...","url_abs":"https://arxiv.org/abs/2507.13895","url_pdf":"https://arxiv.org/pdf/2507.13895v1","authors":"[\"Damiano Azzolini\",\"Marco Duca\",\"Stefano Forti\",\"Francesco Gallo\",\"Antonio Ielo\"]","published":"2025-07-18T13:20:58Z","proceeding":"cs.LO","tasks":"[\"cs.LO\",\"cs.DC\"]","methods":"[]","has_code":false}