RDMA-empowered cloud services are gradually deployed across datacenters (DCs) with multiple paths, which exhibit new properties of path asymmetry, delayed congestion signals, and simultaneous flow routing collisions, and further fail existing routing methods.

BGP is the most widely employed inter-AS routing protocol, connecting millions of ASes worldwide. While it is possible to select the egress for outgoing flows based on administrators’ configurations, such schemes are localized due to the privacy of the intra-AS network state. TheAS_Pathfield of BGP records all crossed ASes, which can be used to prevent routing loops and select paths,i.e., selecting the minimum AS-hop path among available paths. Although this scheme is simple, effective, and offers a certain degree of global perspective, selecting paths at AS granularity ignores the transmission performance within each intra-AS, which may result in selecting non-optimal routing paths. To enable the use of private intra-AS data for inter-AS routing, we proposed a privacy-preserving intra-AS state-assisted inter-AS routing scheme, which can select optimal inter-AS paths without disclosing specific intra-AS state data. Specifically, we added an additional BGP header field to carry path performance features and designed a three-step data masking mechanism to protect intra-AS state data, enabling the selection of inter-AS paths with intra-AS state awareness. I2BGP has been deployed in the Greater Bay Area Future Network and a large-scale network simulator based on real network topologies. The results show that I2BGP outperforms BGP in terms of specified forwarding hops, delay, and bandwidth metrics.

With the increasing demand for high-performance computing (HPC) and artificial intelligence (AI), the transmission rates of data center networks have rapidly increased. However, this rise in link rate has not been accompanied by a proportional increase in switch buffer sizes, leading to an increase in the number of small flows and exacerbating buffer overflow problems. Traditional TCP and PFC mechanisms are ineffective in alleviating the flow completion time (FCT) issues of small flows. To address this, this paper proposes an improved solution based on the Link Local Retransmission (LLR) technique, called LLR-CoLoR. The approach works by backing up packets during congestion and precisely retransmitting lost packets, thereby avoiding the negative impacts of the PFC mechanism. This significantly reduces the FCT of small flows and improves the overall performance of data center networks. Large-scale topology experiments conducted on NS3 show that LLR-CoLoR reduces the FCT slowdown of small flows by more than 4X compared to other algorithms.

With the explosive growth in the scale and complexity of large language models (LLMs), there is an urgent need to extend training and inference workloads from within a single data center to across multiple data centers. However, this also introduces new challenges for network transport protocols. To address these issues, we propose SRCC (Sub-RTT Congestion Control), a method designed for inter-datacenter networks. Specifically, SRCC introduces a flowset-based mechanism along with shared node tables, enabling Datacenter Interconnect (DCI) switches to be aware of the path status of each flow. By leveraging information shared among different flows, SRCC can accurately adjust the sending rate at a sub-RTT timescale, thereby significantly improving network performance. Building on this approach, we design detailed mechanisms to address the following challenges: (1) applying INT technology in wide-area networks; (2) acquiring INT information with low overhead; and (3) achieving precise congestion window adjustments under sub-RTT perception.We conducted large-scale simulations using NS3, and the experimental results show that our scheme reduces the average FCT slowdown by 44.17% and 53.86% compared to HPCC and DCTCP, respectively.

Large-scale incast traffic generated by AI training tasks poses significant challenges to RDMA networks. Existing congestion control mechanisms, such as Artificial Intelligence ECN (AI ECN) and fixed-ratio PFC, struggle to mitigate frequent PFC pauses caused by ECN's delayed feedback. This paper proposes DSCC, a synergistic algorithm of PFC and ECN. Based on the AI ECN algorithm, DSCC adjusts the PFC threshold according to the incast degree. The adjustment process adheres to the threshold constraints of PFC and ECN. Experiments show that DSCC can significantly improve network performance.