Jun 2026· arXiv.org· Vol abs/2606.25553· 0 citations· 45 references
Computer Science
TL;DR
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.
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.
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
Modern networked systems are progressively adopting network virtualization and Service Function Chaining (SFC) to deliver scalable, flexible and low-latency services. A central challenge in this context is the Service Function Chain Placement Problem (SFC-PP), which involves efficiently placing Virtualized Network Functions (VNFs) under stringent resource constraints and Quality of Service (QoS) requirements in dynamic network environments. To address this, we propose an enhanced Proximal Policy Optimization (PPO) framework for resource-aware and latency-sensitive SFC placement in edge-enabled networks. Our approach incorporates network performance metrics into the PPO learning pipeline through delay-aware reward design, resource-aware state representation and feasibility-aware action masking, thereby improving policy stability and infrastructure utilization. Extensive evaluations on multiple topologies including RNET, NSFNET, GBN and GEANT2 demonstrate that the proposed PPO significantly outperforms the Random Selection (RS), Q-Learning (QL) and Deep Q-Network (DQN) baselines, achieving up to 31% higher service acceptance, with reduced service latency and improved resource efficiency. A healthcare-oriented case study demonstrates the applicability of the proposed framework in mission-critical and latency-sensitive service environments. These results highlight PPO as a robust and scalable solution for intelligent service orchestration in dynamic network environments.
Nithin Melala Eshwarappa, Ching-Hsien Hsu, Hojjat Baghban et al.· ACM Transactions on Modeling...· 0 citations
This paper employs five lightweight machine learning models to predict query-specific latency, accuracy, resource usage, and response size for each model-size-quantization-device combination, and design a lightweight heuristic that approximates the BILP solution.
Reza Farahani, Zoha Azimi, Mario Colosi et al.· 0 citations
Edge computing is a promising paradigm for deploying latency-sensitive applications (Apps) as it brings resources closer to end users. Edge Apps often adopt a microservice (MS) architecture, breaking monolithic Apps into lightweight, containerized MSs that can be dynamically and independently deployed. However, managing such Apps involves three key challenges: (i) optimizing the placement of MSs to reduce both response time and resource overhead, (ii) handling MS migration or relocation as users move while minimizing App service disruption (App downtime), and (iii) enabling MS sharing across Apps while ensuring performance guarantees. We formulate this as an optimization problem, named Multi-microservice Application Placement (MAP), prove its NP-hardness, and introduce STEP (State and Topology-aware Edge-MS Placement), a polynomial-time heuristic. STEP distinguishes itself from prior work by: (i) jointly considering stateful and stateless MS characteristics in deployment decisions, (ii) exploiting MS shareability to reduce resource usage, (iii) balancing response latency, App downtime, and resource utilization, and (iv) leveraging multiple versions of the same MS to adapt quality of service to available edge resources. Our results in a small-scale scenario show that STEP achieves near-optimal performance with only 7% higher CPU cost than the optimal solution. Large-scale real-time experiments on a Kubernetes cluster demonstrate that STEP consistently outperforms competing methods, achieving up to 50% lower deployment costs while delivering 50% gain in app quality and saving 15% in radio resources with over 90% request success rates.
Madhura Adeppady, Yenchia Yu, Ali Rahmanian et al.· IEEE Transactions on Network...· 0 citations
With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommendation, indoor navigation, and book localization. To address the limitations of cloud-only processing, this paper investigates task-offloading optimization in a cloud-assisted mobile edge computing environment for smart library services. A three-tier cloud–edge–device collaborative architecture is first established, and the task-offloading problem is formulated as a multi-objective optimization problem that jointly minimizes task-completion delay and user-side energy consumption under latency, resource-capacity, and coverage constraints. To solve the dynamic decision-making problem, a preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning. Simulation results show that PA-DDQN achieves better performance than fixed offloading strategies and representative reinforcement-learning baselines. Under the heaviest task load, PA-DDQN reduces the average task-completion delay by 23.1% and 31.0% compared with D3QN and DDQN, respectively, while reducing energy consumption by 5.8% and 9.9%. It also improves the task success rate by 14.8% and 21.7%, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems.
Jingjing Qu, Peiying Zhang, Ruixin Wang et al.· Information· 0 citations
Mobile edge computing (MEC) has emerged as a promising paradigm to support latency-sensitive applications by deploying computing and storage resources closer to end users. Service caching at the network edge is an effective approach to further reduce response latency and improve quality of service (QoS). However, the increasing scale and dynamic nature of user requests pose significant challenges to efficient service caching and updating under limited edge resources and cost budgets. In this article, we investigate the joint optimization problem of service caching and dynamic updating in a cloud–edge–end collaborative architecture, with the objective of minimizing the average service access latency of users. We first construct a comprehensive system model that captures communication latency, service response delay, and the costs associated with service caching and updates. The problem is formulated as a nonlinear integer programming problem and addressed via a two-stage solution framework. Specifically, in the initial caching stage, we employ Lagrangian relaxation to reduce the high-dimensional knapsack problem and design an efficient algorithm integrated with Tabu Search. In the dynamic updating stage, we develop a Monte Carlo tree search (MCTS)-based algorithm enhanced by service popularity awareness, along with a novel service replacement strategy that considers user preference and service reliability. Extensive experiments under various system scales demonstrate that the proposed approach significantly outperforms state-of-the-art methods in terms of average service latency, cost budget satisfaction, and update efficiency.
Tianyang Zheng, Pengfei Yang, Kang Shen et al.· IEEE Internet of Things Jour...· 0 citations