“DRL+ FL”: An intelligent resource allocation model based on deep reinforcement learning for mobile edge computing
N Shan, X Cui, Z Gao - Computer Communications, 2020 - Elsevier
With the emergence of a large number of computation-intensive and time-sensitive
applications, smart terminal devices with limited resources can only run the model training …
applications, smart terminal devices with limited resources can only run the model training …
Smart resource allocation for mobile edge computing: A deep reinforcement learning approach
The development of mobile devices with improving communication and perceptual
capabilities has brought about a proliferation of numerous complex and computation …
capabilities has brought about a proliferation of numerous complex and computation …
[HTML][HTML] Deep reinforcement learning for performance-aware adaptive resource allocation in mobile edge computing
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 …
close proximity to mobile users, which enables resource-limited mobile devices to offload …
iRAF: A deep reinforcement learning approach for collaborative mobile edge computing IoT networks
Recently, as the development of artificial intelligence (AI), data-driven AI methods have
shown amazing performance in solving complex problems to support the Internet of Things …
shown amazing performance in solving complex problems to support the Internet of Things …
Deep reinforcement learning based approach for online service placement and computation resource allocation in edge computing
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 …
devices, edge computing has been put forward as an extension of cloud computing, to …
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 …
services physically and logically close to the users rather than using the cloud for hosting …
Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
A collaborative optimization strategy for computing offloading and resource allocation based on multi-agent deep reinforcement learning
Y Jiang, Y Mao, G Wu, Z Cai, Y Hao - Computers and Electrical …, 2022 - Elsevier
With the emergence of mobile edge computing (MEC), the edge cloud with certain
computing power is deployed closer to the mobile device, which can well solve the …
computing power is deployed closer to the mobile device, which can well solve the …
DRJOA: intelligent resource management optimization through deep reinforcement learning approach in edge computing
Mobile edge computing (MEC) can enhance the computation capabilities of smart mobile
devices for computation-intensive mobile applications via supporting computation offloading …
devices for computation-intensive mobile applications via supporting computation offloading …
Resource allocation based on deep reinforcement learning in IoT edge computing
By leveraging mobile edge computing (MEC), a huge amount of data generated by Internet
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …
of Things (IoT) devices can be processed and analyzed at the network edge. However, the …