Resource management in computing is a very challenging problem that involves making
sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse …

This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …

Driven by the emergence of new compute-intensive applications and the vision of the
Internet of Things (IoT), it is foreseen that the emerging 5G network will face an …

Sending data to the cloud for analysis was a prominent trend during the past decades,
driving cloud computing as a dominant computing paradigm. However, the dramatically …

With the explosive growth of mobile data and the unprecedented demand for computing
power, resource-constrained edge devices cannot effectively meet the requirements of …

Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles
to provide computing resources for end users and relieve heavy traffic burden for cellular …

Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT
networks, and vehicular networks running different specific applications such as Augmented …

In the IoT-based systems, the fog computing allows the fog nodes to offload and process
tasks requested from IoT-enabled devices in a distributed manner instead of the centralized …

Computation offloading from a mobile device to the edge server is an emerging paradigm to
reduce completion latency of intensive computations in mobile-edge computing (MEC). In …

With the rapid development of mobile devices and deep learning, mobile smart applications
using deep learning technology have sprung up. It satisfies multiple needs of users, network …