AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach
Currently, researchers have motivated a vision of 6G for empowering the new generation of
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G …
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 …
Customized slicing for 6G: Enforcing artificial intelligence on resource management
Next generation wireless networks are expected to support diverse vertical industries and
offer countless emerging use cases. To satisfy stringent requirements of diversified services …
offer countless emerging use cases. To satisfy stringent requirements of diversified services …
“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 …
IoT microservice deployment in edge-cloud hybrid environment using reinforcement learning
The edge-cloud hybrid environment requires complex deployment strategies to enable the
smart Internet-of-Things (IoT) system. However, current service deployment strategies use …
smart Internet-of-Things (IoT) system. However, current service deployment strategies use …
Resource management at the network edge: A deep reinforcement learning approach
With the advent of edge computing, it is highly recommended to extend some cloud services
to the network edge such that the services can be provisioned in the proximity of end users …
to the network edge such that the services can be provisioned in the proximity of end users …
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 …
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 …
AI-based resource management in beyond 5G cloud native environment
5G system and beyond targets a large number of emerging applications and services that
will create extra overhead on network traffic. These industrial verticals have aggressive …
will create extra overhead on network traffic. These industrial verticals have aggressive …
Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach
As an emerging service architecture, microservice enables decomposition of a monolithic
web service into a set of independent lightweight services which can be executed …
web service into a set of independent lightweight services which can be executed …