Data is a precious resource in today's society, and it is generated at an unprecedented and
constantly growing pace. The need to store, analyze, and make data promptly available to a …

Training the deep convolutional neural network for computer vision problems is slow and
inefficient, especially when it is large and distributed across multiple devices. The …

Monotask is a unit of work that uses only a single type of resource (eg, CPU, network, disk
I/O). While monotask was primarily introduced as a means to reason about job performance …

For many years, the distributed systems community has struggled to smooth the transition
from local to remote computing. Transparency means concealing the complexities of …

Workload co-location has become the de-facto approach for hosting applications in Cloud
environments, leading, however, to interference and fragmentation in shared resources of …

Although the distributed machine learning methods can speed up the training of large deep
neural networks, the communication cost has become the non-negligible bottleneck to …

Neural network based recommendation models are widely used to power many internet-
scale applications including product recommendation and feed ranking. As the models …

Video streaming applications (VSAs) are increasingly being deployed on large-scale edge
platforms, which have the potential to significantly improve the quality of service (QoS) and …

Large convolutional neural networks (CNNs) have been successful in data mining tasks, but
it is hard to train these large-scale models. Model parallelism (MP) places a large CNN to …

Synchronous distributed training can scale the training of deep neural networks on large-
scale data, thus it has been widely adopted in large-scale applications. Because it often …