Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Aiming at achieving artificial general intelligence (AGI) for Metaverse, pretrained foundation
models (PFMs), eg, generative pretrained transformers (GPTs), can effectively provide …

Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape
of cloud computing has been undergoing remarkable changes with the emergence of many …

With the rapid development of artificial general intelligence (AGI), various multimedia
services based on pretrained foundation models (PFMs) need to be effectively deployed …

Deep learning models are increasingly used for end-user applications, supporting both
novel features such as facial recognition, and traditional features, eg web search. To …

Increase in demand for intelligent Internet of Things (IoT) services has brought a resource
shortage to the limited resources of edge cloud. We propose to serve intelligent IoT service …

In the telerehabilitation system, the statistical data of the patients' movement are stored in the
temporary storage and synchronised to the storage service of online cloud data. Application …

This paper proposes a novel queueing-theoretic approach to enable stochastic congestion-
aware scheduling for distributed machine learning inference queries. Our proposed …

This paper proposes a novel queueing-theoretic approach to enable congestion-aware
scheduling of distributed network-heavy jobs (eg, machine learning inference). Our …