Complementary to the fancy big data applications, networking for big data is an
indispensable supporting platform for these applications in practice. This emerging research …

Data center networks usually employ the scale-out model to provide high bisection
bandwidth for applications. A large amount of data is required to be transferred frequently …

Inter-coflow scheduling improves application-level communication performance in data-
parallel clusters. However, existing efficient schedulers require a priori coflow information …

Leveraging application-level requirements using coflows has recently been shown to
improve application-level communication performance in data-parallel clusters. However …

We present Sincronia, a near-optimal network design for coflows that can be implemented
on top on any transport layer (for flows) that supports priority scheduling. Sincronia achieves …

Large data centers are currently the mainstream infrastructures for big data processing. As
one of the most fundamental tasks in these environments, the efficient execution of …

In multi-tenant cloud data centers, operators need to ensure that competing tenants and
applications are isolated from each other and fairly share limited network resources. With …

To improve the processing efficiency of jobs in distributed computing, the concept of coflow
is proposed. A coflow is a collection of flows that are semantically correlated in a multi-stage …

Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

By combining edge computing and parallel computing, distributed edge computing has
emerged as a new paradigm to exploit the booming IoT devices at the edge. To accelerate …