12 citations · 22 across the 5 of their papers we have counts for
5 papers
OSP: Boosting Distributed Model Training with 2-stage Synchronization
Zixuan Chen, Lei Shi, Xuandong Liu +3
Distributed deep learning (DDL) is a promising research area, which aims to increase the efficiency of training deep learning tasks with large size of datasets and models. As the c…
SDT: A Low-cost and Topology-reconfigurable Testbed for Network Research
Zixuan Chen, Zhigao Zhao, Zijian Li +3
Network experiments are essential to network-related scientific research (e.g., congestion control, QoS, network topology design, and traffic engineering). However, (re)configuring…
Boosting Distributed Machine Learning Training Through Loss-tolerant Transmission Protocol
Zixuan Chen, Lei Shi, Xuandong Liu +3
Distributed Machine Learning (DML) systems are utilized to enhance the speed of model training in data centers (DCs) and edge nodes. The Parameter Server (PS) communication archite…
RetroFlow: Maintaining Control Resiliency and Flow Programmability for Software-Defined WANs
Zehua Guo, Wendi Feng, Sen Liu +3
Providing resilient network control is a critical concern for deploying Software-Defined Networking (SDN) into Wide-Area Networks (WANs). For performance reasons, a Software-Define…
Joint Switch Upgrade and Controller Deployment in Hybrid Software-Defined Networks
Zehua Guo, Weikun Chen, Ya-Feng Liu +2
To improve traffic management ability, Internet Service Providers (ISPs) are gradually upgrading legacy network devices to programmable devices that support Software-Defined Networ…