Deep reinforcement learning-based server selection for mobile edge computing

H Liu, G Cao - IEEE Transactions on Vehicular Technology, 2021 - ieeexplore.ieee.org
With Mobile Edge Computing (MEC), computational intensive applications can be offloaded
to the nearby edge servers to support latency-sensitive applications on mobile devices …

Dynamic and intelligent edge server placement based on deep reinforcement learning in mobile edge computing

X Jiang, P Hou, H Zhu, B Li, Z Wang, H Ding - Ad Hoc Networks, 2023 - Elsevier
In the era of 5G and beyond, Mobile Edge Computing (MEC) has emerged as a technology
that seamlessly integrates wireless networks and the Internet, enabling low-latency and high …

Smart collaborative optimizations strategy for mobile edge computing based on deep reinforcement learning

J Fang, M Zhang, Z Ye, J Shi, J Wei - Computers & Electrical Engineering, 2021 - Elsevier
With the arrival of the 5th generation mobile networks (5 G) era, the data needed by mobile
devices (MDs) is explosively growing. High-consumption, low-latency applications are huge …

Deep reinforcement learning based approach for online service placement and computation resource allocation in edge computing

T Liu, S Ni, X Li, Y Zhu, L Kong… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Due to the urgent emergence of computation-intensive intelligent applications on end
devices, edge computing has been put forward as an extension of cloud computing, to …

Deep Reinforcement Learning for Performance‐Aware Adaptive Resource Allocation in Mobile Edge Computing

B Huang, Z Li, Y Xu, L Pan, S Wang… - Wireless …, 2020 - Wiley Online Library
Mobile edge computing (MEC) enables to provide relatively rich computing resources in
close proximity to mobile users, which enables resource‐limited mobile devices to offload …

DRL-based resource allocation optimization for computation offloading in mobile edge computing

G Wu, Y Zhao, Y Shen, H Zhang… - IEEE INFOCOM 2022 …, 2022 - ieeexplore.ieee.org
Mobile edge computing (MEC) provides a new development direction for emerging
computing-intensive applications because it can improve computing performance and lower …

Load balancing aware task offloading in mobile edge computing

Y Gao, Z Li - 2022 IEEE 25th International Conference on …, 2022 - ieeexplore.ieee.org
Prompted by the remarkable progress in wireless communications technology and the
explosive growth in the number of mobile devices (MDs), there is an increasing need for …

User allocation in mobile edge computing: A deep reinforcement learning approach

SP Panda, A Banerjee… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
In recent times, the need for low latency has made it necessary to deploy application
services physically and logically close to the users rather than using the cloud for hosting …

Dynamic reservation of edge servers via deep reinforcement learning for connected vehicles

J Zhang, S Chen, X Wang, Y Zhu - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Edge computing is promising for connected vehicles. As vehicles move, their resource
demands for edge servers vary. Thus, it is necessary to reserve edge servers dynamically to …

Cooperative task offloading for mobile edge computing based on multi-agent deep reinforcement learning

J Yang, Q Yuan, S Chen, H He… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Driven by the prevalence of the computation-intensive and delay-intensive mobile
applications, Mobile Edge Computing (MEC) is emerging as a promising solution …