Data-parallel applications generate a mix of coflows with and without deadlines. Deadline
coflows are mission-critical and must be completed within deadlines, while non-deadline …

Data-parallel applications generate a mix of coflows with and without deadlines. Deadline
coflows are mission-critical and must be completed within deadlines, while the non-deadline …

Data-parallel applications run on cluster of servers in a datacenter and their communication
triggers correlated resource demand on multiple links that can be abstracted as coflow. They …

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

In data center networks (DCN), large scale flows produced by parallel computing
frameworks form many coflows semantically. Most inter-coflow schedulers only focus on the …

Emerging distributed applications, such as big data analytics, generate a large number of
flows that concurrently transport data across data center networks. To improve their …

Several cloud or data-parallel applications involve coflow scheduling, which controls a set of
flows under the same semantic meaning with a common goal. Due to such common goal …

In recent years, the coflow abstraction has received significant attentions, for its prominent
ability to capture application semantics. On this basis, multiple coflow scheduling …

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 …

Today's data center jobs typically follow a coflow model. Each coflow consists of multiple
concurrent data flows, while each job is comprised of multiple coflows. Only completing all …