While recent deep learning workload schedulers exhibit excellent performance, it is arduous
to deploy them in practice due to some substantial defects, including inflexible intrusive …

The explosive growth of IP networks, the advent of cloud computing, and the rapid progress
in wireless communications witnessed today reflect significant progress towards meeting the …

The past decade witnessed a remarkable increase in deep learning (DL) workloads which
require GPU resources to accelerate the training process. However, the existing coarse …

This work presents a novel approach to neural architecture search (NAS) that aims to
increase carbon efficiency for the model design process. The proposed framework CE-NAS …

Cloud elasticity involves timely provisioning and de-provisioning of computing resources
and adjusting resources size to meet the dynamic workload demand. This requires fast, and …

With the widespread adoption of deep learning (DL) applications in recent years, training DL
models has become increasingly prevalent. Nevertheless, training these models is typically …

Edge computing is an important group test part in the modern Internet of Things architecture.
Due to the uncertainty of the environment, the assignment algorithm of computing task …

Weather forecasting is an essential predictive challenge that has depended primarily on
model-based methods. Collection of data about the different weather parameters is needed …

Cloud elasticity involves timely provisioning and de-provisioning of computing resources
and adjusting resources size to meet the dynamic workload demand. This requires fast, and …

To achieve elasticity in cloud environment a holistic solution must be considered that
measures all running applications and resources performance, including its cloud …