Combining Declarative and Linear Programming for Application Management in the Cloud-Edge Continuum

2025-04-16

AI Summary

This paper addresses the deployment optimization problem for data-aware multi-service applications in cloud-edge collaborative environments, aiming to jointly minimize operational costs and cross-layer data transfer overhead. Methodologically, it proposes EdgeWiseCR—a declarative modeling framework integrated with Mixed-Integer Linear Programming (MILP)—enhanced by infrastructure constraint preprocessing and a novel continuous inference mechanism that enables reuse of existing deployments, thereby significantly reducing recomputation and service migration frequency. Compared to its predecessor EdgeWise, EdgeWiseCR achieves a 65% speedup in execution time while maintaining high placement stability under concurrent multi-application workloads, heterogeneous topologies, and large-scale dynamic failures. Its core innovation lies in embedding continuous inference within the declarative optimization pipeline, effectively balancing computational efficiency, deployment cost-effectiveness, and environmental adaptability.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Optimize application placement in Cloud-Edge settings
• Minimize operational costs and data transfers
• Improve efficiency with continuous reasoning

Innovation

• Hybrid declarative and MILP for optimal placements
• Continuous reasoning reduces recomputation and migrations
• Dynamic failure handling in large-scale infrastructures