Performance optimization of serverless edge computing function offloading based on deep reinforcement learning
X Yao, N Chen, X Yuan, P Ou - Future Generation Computer Systems, 2023 - Elsevier
It is difficult for resource-constrained edge servers to simultaneously meet the performance
requirements of all the latency-sensitive Internet of Things (IoT) applications in edge …
requirements of all the latency-sensitive Internet of Things (IoT) applications in edge …
MR-DRO: A fast and efficient task offloading algorithm in heterogeneous edge/cloud computing environments
With the rapid development of Internet of Things (IoT) and next-generation communication
technologies, resource-constrained mobile devices (MDs) fail to meet the demand of …
technologies, resource-constrained mobile devices (MDs) fail to meet the demand 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 …
Transfer reinforcement learning for adaptive task offloading over distributed edge clouds
In the big data era, resource-constrained mobile devices generate an overwhelmingly large
amount of data with complex tasks that demand distributed execution. Offloading …
amount of data with complex tasks that demand distributed execution. Offloading …
Task offloading based on LSTM prediction and deep reinforcement learning for efficient edge computing in IoT
In IoT (Internet of Things) edge computing, task offloading can lead to additional
transmission delays and transmission energy consumption. To reduce the cost of resources …
transmission delays and transmission energy consumption. To reduce the cost of resources …
Dependent task offloading for edge computing based on deep reinforcement learning
Edge computing is an emerging promising computing paradigm that brings computation and
storage resources to the network edge, hence significantly reducing the service latency and …
storage resources to the network edge, hence significantly reducing the service latency and …
[HTML][HTML] A multi-layer guided reinforcement learning-based tasks offloading in edge computing
A Robles-Enciso, AF Skarmeta - Computer Networks, 2023 - Elsevier
Abstract The breakthrough in Machine Learning (ML) techniques and the popularity of the
Internet of Things (IoT) has increased interest in applying Artificial Intelligence (AI) …
Internet of Things (IoT) has increased interest in applying Artificial Intelligence (AI) …
Collaborate edge and cloud computing with distributed deep learning for smart city internet of things
City Internet-of-Things (IoT) applications are becoming increasingly complicated and thus
require large amounts of computational resources and strict latency requirements. Mobile …
require large amounts of computational resources and strict latency requirements. Mobile …
Distributed edge computing offloading algorithm based on deep reinforcement learning
Y Li, F Qi, Z Wang, X Yu, S Shao - IEEE Access, 2020 - ieeexplore.ieee.org
As a mode of processing task request, edge computing paradigm can reduce task delay and
effectively alleviate network congestion caused by the proliferation of Internet of things (IoT) …
effectively alleviate network congestion caused by the proliferation of Internet of things (IoT) …
Semi-online computational offloading by dueling deep-Q network for user behavior prediction
Task offloading could optimize computational resource utilization in edge computing
environments. However, how to assign and offload tasks for different behavior users is an …
environments. However, how to assign and offload tasks for different behavior users is an …