Deep reinforcement learning and game theory for computation offloading in dynamic edge computing markets

S Li, X Hu, Y Du - IEEE Access, 2021 - ieeexplore.ieee.org
As a promising paradigm, computation offloading technology can offload computing tasks to
multi-access edge computing (MEC) servers, which is an appealing choice for resource …

Optimization for computational offloading in multi-access edge computing: A deep reinforcement learning scheme

J Wang, H Ke, X Liu, H Wang - Computer Networks, 2022 - Elsevier
Owing to their limited computing power and battery level, wireless users (WUs) can hardly
handle compute-intensive workflows by the local processor. Multi-access edge computing …

A drl-based decentralized computation offloading method: An example of an intelligent manufacturing scenario

S Lu, S Liu, Y Zhu, W Liang, K Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the development of edge computing and 5G, the demand for resource-limited devices
to execute computation-intensive tasks can be effectively alleviated. The research on …

Price and risk awareness for data offloading decision-making in edge computing systems

G Mitsis, EE Tsiropoulou… - IEEE Systems …, 2022 - ieeexplore.ieee.org
The proliferation of multiaccess edge computing (MEC) paradigm has created a challenging
multiuser–multiserver–multiaccess edge computing competitive environment, which brings …

Multi-user computation offloading for mobile edge computing: A deep reinforcement learning and game theory approach

S Liang, H Wan, T Qin, J Li… - 2020 IEEE 20th …, 2020 - ieeexplore.ieee.org
At present, with the development of the Internet of Things (IoT) and the Internet of Everything
(IoE), Mobile edge computing (MEC) is proposed to provide universal and flexible …

Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning

J Wang, J Hu, G Min, W Zhan, Q Ni… - IEEE Communications …, 2019 - ieeexplore.ieee.org
MEC is an emerging paradigm that utilizes computing resources at the network edge to
deploy heterogeneous applications and services. In the MEC system, mobile users and …

A deep reinforcement learning based offloading game in edge computing

Y Zhan, S Guo, P Li, J Zhang - IEEE Transactions on Computers, 2020 - ieeexplore.ieee.org
Edge computing is a new paradigm to provide strong computing capability at the edge of
pervasive radio access networks close to users. A critical research challenge of edge …

A deep reinforcement learning approach for online computation offloading in mobile edge computing

Y Zhang, T Liu, Y Zhu, Y Yang - 2020 IEEE/ACM 28th …, 2020 - ieeexplore.ieee.org
With the explosion of mobile smart devices, many computation intensive applications have
emerged, such as interactive gaming and augmented reality. Mobile edge computing is put …

Deep reinforcement learning-based offloading decision optimization in mobile edge computing

H Zhang, W Wu, C Wang, M Li… - 2019 IEEE Wireless …, 2019 - ieeexplore.ieee.org
As a promising technique, mobile edge computing (MEC) has attracted significant attention
from both academia and industry. However, the offloading decision for computing tasks in …

Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning

X Chen, H Zhang, C Wu, S Mao, Y Ji… - IEEE Internet of Things …, 2018 - ieeexplore.ieee.org
To improve the quality of computation experience for mobile devices, mobile-edge
computing (MEC) is a promising paradigm by providing computing capabilities in close …