Deep reinforcement learning-based task scheduling in iot edge computing
Edge computing (EC) has recently emerged as a promising paradigm that supports resource-
hungry Internet of Things (IoT) applications with low latency services at the network edge …
hungry Internet of Things (IoT) applications with low latency services at the network edge …
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 …
Edge-enabled two-stage scheduling based on deep reinforcement learning for internet of everything
Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic,
which is playing an increasingly indispensable role in modern intelligent applications. These …
which is playing an increasingly indispensable role in modern intelligent applications. These …
Task offloading and resource allocation algorithm based on deep reinforcement learning for distributed AI execution tasks in IoT edge computing environments
Recently, the application of Artificial Intelligence (AI) in the Internet of Things (IoT) devices is
increasing. As these devices are limited in processing and storing massive computations of …
increasing. As these devices are limited in processing and storing massive computations of …
Dynamic task allocation and service migration in edge-cloud iot system based on deep reinforcement learning
Edge computing (EC) extends the ability of cloud computing to the network edge to support
diverse resource-sensitive and performance-sensitive IoT applications. However, due to the …
diverse resource-sensitive and performance-sensitive IoT applications. However, due to the …
D3PG: Dirichlet DDPG for task partitioning and offloading with constrained hybrid action space in mobile-edge computing
Mobile-edge computing (MEC) has been regarded as a promising paradigm to reduce
service latency for data processing in the Internet of Things (IoT) by provisioning computing …
service latency for data processing in the Internet of Things (IoT) by provisioning computing …
Deep reinforcement learning-based workload scheduling for edge computing
T Zheng, J Wan, J Zhang, C Jiang - Journal of Cloud Computing, 2022 - Springer
Edge computing is a new paradigm for providing cloud computing capacities at the edge of
network near mobile users. It offers an effective solution to help mobile devices with …
network near mobile users. It offers an effective solution to help mobile devices with …
DeepEdge: A new QoE-based resource allocation framework using deep reinforcement learning for future heterogeneous edge-IoT applications
Edge computing is emerging to empower the future of Internet of Things (IoT) applications.
However, due to heterogeneity of applications, it is a significant challenge for the edge cloud …
However, due to heterogeneity of applications, it is a significant challenge for the edge cloud …
Online dispatching and fair scheduling of edge computing tasks: A learning-based approach
The emergence of edge computing can effectively tackle the problem of large transmission
delays caused by the long-distance between user devices and remote cloud servers. Users …
delays caused by the long-distance between user devices and remote cloud servers. Users …
Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network
Abstract The fifth-generation (5G) mobile network services have made tremendous growth in
the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are …
the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are …
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