Geographically distributed applications hosted on cloud are becoming prevalent. They run
on cross-datacenter network that consists of multiple data center networks (DCNs) …

In the era of digitization, Data Center Networks (DCN) have emerged as a critical component
supporting infrastructure for cloud computing, big data analytics, online services, and more …

Packet loss due to link corruption is a major problem in large warehouse-scale datacenters.
The current state-of-the-art approach of disabling corrupting links is not adequate because …

Load balancing in data center networks (DCNs) is a crucial and complex undertaking. Multi-
path TCP (MPTCP) has been proposed as a cost-effective solution that aims to distribute …

With Remote Direct Memory Access (RDMA) extended to commercial Ethernet, modern Data
Center Networks (DCNs) carry both traditional TCP and RDMA, to support diversified …

RDMA over Converged Ethernet (RoCE) has been widely used in datacenter networks and
it relies on Priority Flow Control (PFC) to ensure a lossless network. However, PFC brings …

Traditional congestion control algorithms (CCAs) frequently struggle to manage microbursts,
resulting in performance degradation. Although RoCEv2 (RDMA over Converged Ethernet …

This abstract presents an innovative approach that leverages the Gramian Angular Field
(GAF) in conjunction with Convolutional Neural Networks (CNN) to improve the accuracy …

Abstract effectively extract knowledge from large-scale training data. Such large models
have achieved state-of-art accuracy results in various tasks, including but not limited to …

Bulk synchronous parallel (BSP) is the de-facto paradigm for distributed DNN training in
today's production clusters. However, due to the global synchronization nature, its …