Flow scheduling and congestion control are two important techniques to reduce flow
completion time in data center networks. While existing works largely treat them …

Datacenter networks commonly facilitate the transmission of data in distributed computing
frameworks through coflows, which are collections of parallel flows associated with a …

Coflow is a recently proposed networking abstraction to capture communication patterns in
data-parallel computing frameworks. We consider the problem of efficiently scheduling …

A coflow is a collection of related parallel flows that occur typically between two stages of a
multi-stage computing task in a network, such as shuffle flows in MapReduce. The coflow …

Existing flow scheduling schemes for data center networks optimize for a specific workload
and performance metric. In this paper, we present 2D, a new scheduling policy that offers …

Many data center applications perform rich and complex tasks (eg, executing a search query
or generating a user's news-feed). From a network perspective, these tasks typically …

In this paper, we survey different existing schemes for the transmission of flows in Data
Center Networks (DCNs). The transport of flows in DCNs must cope with the bandwidth …

In the data flow models of today's data center applications such as MapReduce, Spark and
Dryad, multiple flows can comprise a coflow group semantically. Only completing all flows in …

Today's data centers act as the primary infrastructure for emerging technologies. QoS
imposes requirements for more attentive techniques that can deal with different …

The sustainable growth of bandwidth has been an inevitable tendency in current Data
Center Networks (DCN). However, the dramatic expansion of link capacity offers a …