Reinforcement learning framework for server placement and workload allocation in multiaccess edge computing
A Mazloomi, H Sami, J Bentahar… - IEEE Internet of …, 2022 - ieeexplore.ieee.org
Cloud computing is a reliable solution to provide distributed computation power. However,
real-time response is still challenging regarding the enormous amount of data generated by …
real-time response is still challenging regarding the enormous amount of data generated by …
Mean field game guided deep reinforcement learning for task placement in cooperative multiaccess edge computing
Cooperative multiaccess edge computing (MEC) is a promising paradigm for the next-
generation mobile networks. However, when the number of users explodes, the …
generation mobile networks. However, when the number of users explodes, the …
Toward reinforcement-learning-based service deployment of 5G mobile edge computing with request-aware scheduling
5G wireless network technology will not only significantly increase bandwidth but also
introduce new features such as mMTC and URLLC. However, high request latency will …
introduce new features such as mMTC and URLLC. However, high request latency will …
Deploying network functions for multiaccess edge-IoT with deep reinforcement learning
Edge computing is a promising technology to empower the Internet of Things (IoT) by
providing additional processing ability, where the tasks can be offloaded to the edge servers …
providing additional processing ability, where the tasks can be offloaded to the edge servers …
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 …
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 …
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 …
Deep reinforcement learning-based server selection for mobile edge computing
With Mobile Edge Computing (MEC), computational intensive applications can be offloaded
to the nearby edge servers to support latency-sensitive applications on mobile devices …
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
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 …
that seamlessly integrates wireless networks and the Internet, enabling low-latency and high …
Joint service migration and resource allocation in edge IoT system based on deep reinforcement learning
Multi-access Edge Computing (MEC) provides services for resource-sensitive and delay-
sensitive Internet of Things (IoT) applications by extending the capabilities of cloud …
sensitive Internet of Things (IoT) applications by extending the capabilities of cloud …
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