In order to describe the roadmap of current edge computing research activities, we first
address a brief overview of the most advanced edge computing surveys published in the last …

The recent migration towards Internet of Things determined the rise of a Computing
Continuum paradigm where Edge and Cloud resources coordinate to support the execution …

It is commonly assumed that the end-to-end networking performance of edge offloading is
purely dictated by that of the network connectivity between end devices and edge computing …

Hardware accelerators such as GPUs are required for real-time, low-latency inference with
Deep Neural Networks (DNN). However, due to the inherent limits to the parallelism they …

Smart resource allocation is essential for optimising cloud computing efficiency and
utilisation, but it is also very challenging as traditional approaches often overprovision CPU …

Smart city applications are revolutionizing the way people interact with diverse systems in
city-wide applications. Internet of Things (IoT) and machine learning are two enabling …

In the realm of edge computing, the increasing demand for high Quality of Service (QoS),
particularly in dynamic multimedia streaming applications (eg, Augmented Reality/Virtual …

This study focuses on examining the shift of an application system from a traditional
monolithic architecture to a cloud-native microservice architecture (MSA), with a specific …

Traditionally, multi-tenant cloud and edge platforms use fair-share schedulers to fairly
multiplex resources across applications. These schedulers ensure applications receive …

The artificial intelligence (AI) industry is increasingly integrating with diverse sectors such as
smart logistics, FinTech, entertainment, and cloud computing. This expansion has led to the …