Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI
refers to the practice of doing AI computations near the users at the network's edge, instead …

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

Over the past six decades, the computing systems field has experienced significant
transformations, profoundly impacting society with transformational developments, such as …

The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

Energy efficiency is a key area of research aimed at achieving sustainable and
environmentally friendly networks. With the rise in data traffic and network congestion, IoT …

Fog computing paradigm provides diverse processing resources and storage close to the
edge of Internet of Things (IoT) networks. Workflow scheduling is an open issue in fog …

Abstract The Internet of Things (IoT) is constantly evolving. The variety of IoT applications
has caused new demands to emerge on users' part and competition between computing …

The use of cloud computing service models is rapidly increasing, but inefficient resource
usage in cloud data centers can lead to great energy consumption and costs. To address …

Today, fog and cloud computing environments can be used to further develop the Internet of
Things (IoT). In such environments, task scheduling is very efficient for executing user …

In fog computing, inefficient scheduling of user tasks causes more delays. Moreover, how to
schedule tasks that need to be offloaded to fog nodes or cloud nodes has not been fully …