Towards Efficient and Scalable RDMA Networking for Datacenter Applications

PhD Thesis Proposal Defence


Title: "Towards Efficient and Scalable RDMA Networking for Datacenter 
Applications"

by

Miss Wenxue LI


Abstract:

Remote Direct Memory Access (RDMA) has become a cornerstone of high-speed 
networking in modern datacenters. As applications such as AI training and HPC 
continue to scale, datacenter networks demand both higher performance and 
stronger scalability. However, existing RDMA techniques face key limitations, 
including sluggish congestion handling, inflexible communication semantics, 
and poor network scalability, which collectively constrain datacenter 
efficiency.

This thesis addresses these challenges with three contributions. First, we 
propose FlowSail to enable timely congestion handling. By adopting hop-by-hop 
flow regulation without requiring per-flow queues, it achieves sub-RTT 
responsiveness while remaining practical for deployment. Second, we design 
Cepheus, which leverages programmable switches to extend RDMA semantics from 
one-to-one to one-to-many. Through in-network connection bridging and signal 
aggregation, it minimizes transmission hops and maximizes bandwidth 
utilization for one-to-many communication. Finally, we introduce DCP, which 
revisits RDMA reliability for lossy fabrics. By integrating lightweight 
switch-assisted packet trimming with redesigned RDMA NIC reliability logic, 
it enables fast and precise loss recovery under per-packet multipath 
transmission, enabling scalable and efficient RDMA transmission over lossy 
networks.

Together, these contributions advance RDMA networking by improving congestion 
handling, enriching communication semantics, and enabling scalable 
transmission over lossy fabrics, thereby strengthening the foundation for 
future datacenter networks.


Date:                   Thursday, 25 September 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 4472
                        Lifts 25/26

Committee Members:      Prof. Kai Chen (Supervisor)
                        Prof. Song Guo (Chairperson)
                        Dr. Binhang Yuan