Tasks in a data-parallel job communicate with each other through a number of concurrent
flows, which is described as a coflow. These flows are correlated in the sense that the …

Big data analytics in datacenters often involve scheduling of data-parallel jobs. Traditional
scheduling techniques based on improving network resource utilization are subject to …

Reducing Coflow Completion Time (CCT) has a significant impact on data-intensive
application performance in datacenter networks. An efficient allocation of network resources …

Recently, coflow has been proposed as a new abstraction to capture the communication
patterns in a rich set of data parallel applications in data centers. Coflows effectively model …

Link utilization has received extensive attention since data centers become the most
pervasive platform for data-parallel applications. A specific job of such applications involves …

In current data centers, an application (eg, MapReduce, Dryad, search platform, etc.) usually
generates a group of parallel flows to complete a job. These flows compose a coflow and …

Coflow scheduling can improve application-level communication performance for data-
parallel clusters. However, most prior coflow scheduling schemes are based on the …

Traditional network resource management mechanisms are mainly flow or packet based.
Recently, coflow has been proposed as a new abstraction to capture the communication …

Datacenter networked applications, often require multiple data transfer flows that
semantically constitute a coflow group. A coflow is thus considered completed when all the …

This paper presents D2-CAS, a novel decentralized coflow scheduling system, to minimize
the rate of deadline missed coflow for datacenter networks. To design D2-CAS, we first …