Coflow: a networking abstraction for cluster applications

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Making Sense of Performance in Data Analytics Frameworks (2015, background): “Researchers have used the argument that data-intensive application performance is closely tied to datacenter network performance to justify a wide variety of network optimizations [6,14,17–21,27,28,41,42,48,53].”
Efficient coflow scheduling with Varys (2014, positive): “The recently proposed coﬂow abstraction [16] represents such collections of parallel ﬂows to convey job-speciﬁc communication requirements – for example, minimizing completion time or meeting a deadline – to the network and enables application-aware network scheduling (§2).”
Efficient Coflow Scheduling Without Prior Knowledge (2015, background): “1 Background Non-Clairvoyant Coflows A coflow is a collection of parallel flows with distributed endpoints, and it completes after all its flows have completed [18, 20, 19].”
Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics (2016, background): “These patterns are not specific to advanced analytics jobs and have been studied before [30, 20].”