Continuous integration enables the development of microservices-based applications using
container virtualization technology. Container orchestration systems such as Kubernetes …

Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

Many networking tasks now employ deep learning (DL) to solve complex prediction and
optimization problems. However, current design philosophy of DL-based algorithms entails …

As cloud services expand, the need to improve the performance of data center infrastructure
becomes more important. High-performance computing, advanced networking solutions …

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 …

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

Data-parallel training is widely used for scaling DNN training over large datasets, using the
parameter server or all-reduce architecture. Communication scheduling has been promising …

Scheduling plays a pivotal role in cloud computing systems. Designing an efficient
scheduler is a challenging task. The challenge comes from several aspects, including the …

5G system and beyond targets a large number of emerging applications and services that
will create extra overhead on network traffic. These industrial verticals have aggressive …

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 …