In many scenarios (eg, hurricanes, earthquake, rural areas), edge devices cannot access
the cloud, which makes the cloud deep learning (DL) training approach inapplicable …

The cloud is still a popular platform for distributed deep learning (DL) training jobs since
resource sharing in the cloud can improve resource utilization and reduce overall costs …

This paper proposes ElasticFlow, an elastic serverless training platform for distributed deep
learning. ElasticFlow provides a serverless interface with two distinct features:(i) users …

With the emergence of edge devices along with their local computation advantage over the
cloud, distributed deep learning (DL) training on edge nodes becomes promising. In such a …

Edge-cloud jobs are rapidly prevailing in many application domains, posing the challenge of
using both resource-strenuous edge devices and elastic cloud resources. Efficient resource …

Nowdays, it is prevalent to train deep learning (DL) models in cloud-native platforms that
actively leverage containerization and orchestration technologies for high elasticity, low and …

One of the major challenges in distributed deep learning is attenuating straggler problem.
The straggler increases synchronization latency and significantly inhibits the convergence of …

Deep neural network (DNN) shows great promise in providing more intelligence to
ubiquitous end devices. However, the existing partition-offloading schemes adopt data …

As more distributed deep learning (DDL) jobs run in public clouds, their effective scheduling
becomes a major challenge. Current studies prioritize the execution of jobs with less …

Under the paradigm of Edge Computing, the enormous data generated at the network edge
can be processed locally. Machine learning methods are often adopted to make full …