Application Placement with Constraint Relaxation

2025-07-18

AI Summary

In cloud-edge collaborative environments, deploying multi-service applications poses a combinatorial optimization challenge: solutions must simultaneously satisfy functional constraints (e.g., service dependencies) and non-functional constraints (e.g., latency, resource limits), yet constraint infeasibility frequently arises. To address this, we propose an elastic service placement method based on Answer Set Programming (ASP). This is the first work to leverage ASP’s declarative constraint modeling and optimization capabilities for cloud-edge deployment, enabling explicit specification of hard constraints and soft preferences, along with a systematic constraint relaxation mechanism to generate near-optimal feasible solutions. Evaluated in a realistic simulation environment, our approach efficiently computes deployments that fully or approximately satisfy all constraints within practical time bounds. Results demonstrate significant improvements in deployment feasibility and adaptability, while providing DevOps teams with interpretable, controllable, and explainable decision support for service orchestration.

Technology Category

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

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
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Optimizing service placement in cloud-edge networks
• Handling unsatisfiable constraints and DevOps preferences
• Using Answer Set Programming for combinatorial optimization

Innovation

• Uses Answer Set Programming for optimization
• Handles unsatisfiable problem instances
• Relaxes constraints for feasible solutions