Task-driven resource assignment in mobile edge computing exploiting evolutionary computation
The IoT network allows IoT devices to communicate with other devices, applications, and
services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC …
services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC …
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 …
HetMEC: Latency-optimal task assignment and resource allocation for heterogeneous multi-layer mobile edge computing
Driven by great demands on low-latency services of the edge devices (EDs), mobile edge
computing (MEC) has been proposed to enable the computing capacities at the edge of the …
computing (MEC) has been proposed to enable the computing capacities at the edge of the …
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 …
A DRL-driven intelligent joint optimization strategy for computation offloading and resource allocation in ubiquitous edge IoT systems
Intelligent computation offloading and resource allocation for mobile users (MUs) in
ubiquitous edge Internet of Things (IoT) systems is a worthy research hotspot. To improve …
ubiquitous edge Internet of Things (IoT) systems is a worthy research hotspot. To improve …
“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 …
Q-learning algorithm for joint computation offloading and resource allocation in edge cloud
The advent of 5G technology along with the high proliferation of mobile devices entail an
explosion of mobile traffic. Due to their resource-limitation constraint, mobile devices resort …
explosion of mobile traffic. Due to their resource-limitation constraint, mobile devices resort …
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 …
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 …
Latency and reliability-aware workload assignment in IoT networks with mobile edge clouds
N Kherraf, S Sharafeddine, CM Assi… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Along with the dramatic increase in the number of IoT devices, different IoT services with
heterogeneous QoS requirements are evolving with the aim of making the current society …
heterogeneous QoS requirements are evolving with the aim of making the current society …