Graph-based deep learning for communication networks: A survey

W Jiang - Computer Communications, 2022 - Elsevier
Communication networks are important infrastructures in contemporary society. There are
still many challenges that are not fully solved and new solutions are proposed continuously …

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

R Boutaba, MA Salahuddin, N Limam, S Ayoubi… - Journal of Internet …, 2018 - Springer
Abstract Machine Learning (ML) has been enjoying an unprecedented surge in applications
that solve problems and enable automation in diverse domains. Primarily, this is due to the …

Recent advances of resource allocation in network function virtualization

S Yang, F Li, S Trajanovski… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Network Function Virtualization (NFV) has been emerging as an appealing solution that
transforms complex network functions from dedicated hardware implementations to software …

Learning combinatorial optimization on graphs: A survey with applications to networking

N Vesselinova, R Steinert, DF Perez-Ramirez… - IEEE …, 2020 - ieeexplore.ieee.org
Existing approaches to solving combinatorial optimization problems on graphs suffer from
the need to engineer each problem algorithmically, with practical problems recurring in …

Adaptive VNF scaling and flow routing with proactive demand prediction

X Fei, F Liu, H Xu, H Jin - IEEE INFOCOM 2018-IEEE …, 2018 - ieeexplore.ieee.org
With the evolution of Network Function Virtual-izaiton (NFV), enterprises are increasingly
outsourcing their network functions to the cloud. However, using virtualized network …

Topology-aware prediction of virtual network function resource requirements

R Mijumbi, S Hasija, S Davy, A Davy… - … on Network and …, 2017 - ieeexplore.ieee.org
Network functions virtualization (NFV) continues to gain attention as a paradigm shift in the
way telecommunications services are deployed and managed. By separating network …

Applications of machine learning in networking: a survey of current issues and future challenges

MA Ridwan, NAM Radzi, F Abdullah, YE Jalil - IEEE access, 2021 - ieeexplore.ieee.org
Communication networks are expanding rapidly and becoming increasingly complex. As a
consequence, the conventional rule-based algorithms or protocols may no longer perform at …

基于深度强化学习的组合优化研究进展

李凯文, 张涛, 王锐, 覃伟健, 贺惠晖, 黄鸿 - 自动化学报, 2021 - aas.net.cn
组合优化问题广泛存在于国防, 交通, 工业, 生活等各个领域, 几十年来, 传统运筹优化方法是解决
组合优化问题的主要手段, 但随着实际应用中问题规模的不断扩大, 求解实时性的要求越来越高 …

AI-enabled reliable QoS in multi-RAT wireless IoT networks: Prospects, challenges, and future directions

K Zia, A Chiumento… - IEEE Open Journal of the …, 2022 - ieeexplore.ieee.org
Wireless IoT networks have seen an unprecedented rise in number of devices,
heterogeneity and emerging use cases which led to diverse throughput, reliability and …

Safeguard network slicing in 5G: A learning augmented optimization approach

X Cheng, Y Wu, G Min, AY Zomaya… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
Network slicing, as a key 5G enabling technology, is promising to support with more
flexibility, agility, and intelligence towards the provisioned services and infrastructure …