Combining declarative and linear programming for application management in the cloud-edge continuum

•Continuous reasoning mechanism to reduce redundant computation and service migrations in edge deployments.•Extended open-source prototype consisting of a pipeline combining declarative logic, MILP optimisation, and continuous reasoning.•Experimental assessment involving dynamic, large-scale edge sc...

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Veröffentlicht in:Future generation computer systems Jg. 176; S. 108224
Hauptverfasser: Massa, Jacopo, Forti, Stefano, Dazzi, Patrizio, Brogi, Antonio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2026
Schlagworte:
Application Placement
Cloud-Edge Continuum
Continuous Reasoning
Declarative Programming
Mixed-Integer Linear Programming
Cloud-Edge Continuum
Declarative Programming
Application Placement
Mixed-Integer Linear Programming
Continuous Reasoning
ISSN:0167-739X
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Zusammenfassung:•Continuous reasoning mechanism to reduce redundant computation and service migrations in edge deployments.•Extended open-source prototype consisting of a pipeline combining declarative logic, MILP optimisation, and continuous reasoning.•Experimental assessment involving dynamic, large-scale edge scenarios with up to 2048 nodes, and three multi-component applications. [Display omitted] This work investigates the data-aware multi-service application placement problem in Cloud-Edge settings. We previously introduced EdgeWise, a hybrid approach that combines declarative programming with Mixed-Integer Linear Programming (MILP) to determine optimal placements that minimise operational costs and unnecessary data transfers. The declarative stage pre-processes infrastructure constraints to improve the efficiency of the MILP solver, achieving optimal placements in terms of operational costs, with significantly reduced execution times. In this extended version, we improve the declarative stage with continuous reasoning, presenting EdgeWiseCR, which enables the system to reuse existing placements and reduce unnecessary recomputation and service migrations. In addition, we conducted an expanded experimental evaluation considering multiple applications, diverse network topologies, and large-scale infrastructures with dynamic failures. The results show that EdgeWiseCR achieves up to 65 % faster execution compared to EdgeWise, while preserving placement stability under dynamic conditions.
ISSN:0167-739X
DOI:10.1016/j.future.2025.108224