Fog computing is a promising computing paradigm that extends cloud computing to the
edge of networks where it provides services closer to users characterized by closer proximity …

Abstract In current Data center Networks (DCNs), Equal-Cost Multipath (ECMP) is a default
load-balancing scheme. However, using ECMP may result in the rapid growth of Flow …

Distributed machine learning is a mainstream system to learn insights for analytics and
intelligence services of many fronts (eg, health, streaming and business) from their massive …

Recently, the abstraction of coflow is introduced to capture the collective data transmission
patterns among modern distributed data-parallel applications. During processing, coflows …

Certain new ascendant data center workloads can absorb some degradation in network
service, not needing fully reliable data transport and/or their fair-share of network bandwidth …

Accelerating Distributed Deep Learning draws increasing attention recently. Existing
solutions like gradient compression, pipelining computation/communication, flow scheduling …

Recently, the abstraction of coflow is introduced to capture the collective data transmission
patterns among modern distributed data-parallel application. During processing, coflows …

Networked systems are tasked with processing large amounts of data and timely responding
to requests coming in at high rates. These tasks involve the collaboration and …

In recent years, researchers have paid more attention to distributed deep learning to train
large-scale models with massive data in parallel, which is much more efficient than training …