Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems
In cyber-physical systems, it is advantageous to leverage cloud with edge resources to
distribute the workload for processing and computing user data at the point of generation …
distribute the workload for processing and computing user data at the point of generation …
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 …
Multi-agent deep reinforcement learning for cooperative offloading in cloud-edge computing
A Suzuki, M Kobayashi - ICC 2022-IEEE International …, 2022 - ieeexplore.ieee.org
Edge computing is a new paradigm to provide computing capability at the edges close to
end devices. A significant research challenge in edge computing is finding an efficient task …
end devices. A significant research challenge in edge computing is finding an efficient task …
Joint offloading and resource allocation for hybrid cloud and edge computing in SAGINs: A decision assisted hybrid action space deep reinforcement learning …
In recent years, the amalgamation of satellite communications and aerial platforms into
space-air-ground integrated network (SAGINs) has emerged as an indispensable area of …
space-air-ground integrated network (SAGINs) has emerged as an indispensable area of …
DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing
With the explosive growth of mobile data and the unprecedented demand for computing
power, resource-constrained edge devices cannot effectively meet the requirements of …
power, resource-constrained edge devices cannot effectively meet the requirements of …
Multi-resource interleaving for task scheduling in cloud-edge system by deep reinforcement learning
X Pei, P Sun, Y Hu, D Li, L Tian, Z Li - Future Generation Computer Systems, 2024 - Elsevier
Collaborative cloud–edge computing has been systematically developed to balance the
efficiency and cost of computing tasks for many emerging technologies. To improve the …
efficiency and cost of computing tasks for many emerging technologies. To improve the …
“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 …
Multitask multiobjective deep reinforcement learning-based computation offloading method for industrial Internet of Things
J Cai, H Fu, Y Liu - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Edge computing has emerged as a promising paradigm to deploy computing resources to
the network edge. However, most existing computation offloading strategies consider only …
the network edge. However, most existing computation offloading strategies consider only …
Deep reinforcement learning and markov decision problem for task offloading in mobile edge computing
X Gao, MC Ang, SA Althubiti - Journal of Grid Computing, 2023 - Springer
Abstract Mobile Edge Computing (MEC) offers cloud-like capabilities to mobile users,
making it an up-and-coming method for advancing the Internet of Things (IoT). However …
making it an up-and-coming method for advancing the Internet of Things (IoT). However …
Deep reinforcement learning‐based multitask hybrid computing offloading for multiaccess edge computing
J Cai, H Fu, Y Liu - International Journal of Intelligent Systems, 2022 - Wiley Online Library
By deploying computing units in edge servers, the device‐generated computation‐intensive
tasks can be offloaded from the cloud, lessening the core network's traffic and reducing the …
tasks can be offloaded from the cloud, lessening the core network's traffic and reducing the …