Latency-Aware Service Placement using Neural Combinatorial Optimisers for Edge--Cloud Systems

Abstract

The growth of Internet of Things (IoT) applications and latency-sensitive services has increased the demand for efficient service placement across compute continuum platforms, such as edge--cloud systems. Modern applications are decomposed into interdependent microservices deployed over heterogeneous infrastructures, making placement under resource and network constraints an intractable NP-hard combinatorial optimisation problem. This study proposes a latency-aware Edge Placement Neural Combinatorial Optimiser (EP-NCO), a learning-based framework for service placement in compute continuum platforms. EP-NCO employs a dual-graph model to capture resource relationships and service dependencies within both computing infrastructure and application structure. Graph neural networks (GNNs) learn structural embeddings of infrastructure nodes and service components, whereas reinforcement learning policies construct feasible placements that account for execution latency, communication link delays, and bandwidth-sharing effects. Extensive simulations across multiple system scales demonstrate that EP-NCO consistently achieves high-quality placement decisions, reducing the total service response time by 46%--50% compared with metaheuristics (genetic algorithm and particle swarm optimisation) and by 25%--35% compared with controlled RL ablation baselines. Once trained, EP-NCO enables fast online inference, making it a practical solution for dynamic large-scale edge--cloud environments with hundreds of computing nodes, hosting thousands of applications, which is significantly beyond the capability of current scheduling systems.