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 …

Nowadays large-scale distributed machine learning systems have been deployed to support
various analytics and intelligence services in IT firms. To train a large dataset and derive the …

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 …

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

摘要近年来, 流量调度已经发展成为网络领域的热点研究问题. 该问题主要决定何时以及以多大
速率传输网络中的每条数据流, 其对网络性能和应用性能都具有十分重要的影响. 然而 …

In data-parallel computing frameworks, intermediate parallel data is often produced at
various stages which needs to be transferred among servers in the datacenter network (eg …

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 …

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

Distributed machine learning (DML) is an increasingly important workload. In a DML job,
each communication phase can comprise a coflow, and there are dependencies among its …

Edge computing systems typically handle a wide variety of applications that exhibit diverse
degrees of sensitivity to job latency. Therefore, a multitude of utility functions of the job …