With the development of network-intensive applications like machine learning and cloud storage, there are two growing trends: (i) RDMA has been widely deployed to enhance underlying high-speed networks; (ii) applications are deployed on geographically distributed datacenters to meet customer demands (e.g., low access latency to services or regular data backups). To fully utilize the benefits of RDMA, we desire to support long-haul RDMA transport for cross-datacenter applications. Different from common intra-datacenter communications, the hybrid of long-haul and intra-datacenter traffic complicates the congestion state, and the considerably long control loop makes it more severe. We revisit existing congestion control methods and find they are insufficient to address the hybrid traffic congestion.Note that regional datacenters are connected by dedicated long-haul optical fiber and datacenter interconnection (DCI) switches directly. In this paper, we propose Bilateral Congestion Control (BiCC), a novel solution relying on two-side DCI-switches to bilaterally alleviate the hybrid traffic congestion in the sender-side and receiver-side datacenter while serving as a building block for existing host-driven methods. BiCC can shorten the control loop to a single datacenter scale and aggregate congestion information across the whole datacenter. We implement BiCC on commodity P4-based switches and conduct evaluations using both testbed experiments and NS3 simulations. The extensive evaluation results show that BiCC ensures fast congestion avoidance. Thus, BiCC reduces the average FCT for intra-datacenter and inter-datacenter traffic by up to 53% and 51%, respectively, in large-scale simulations.

Coded computing has received significant attention thanks to its advantage in alleviating the straggler effect in distributed computation framework, which would be one of the key fundamental techniques to enable the distributed and decentralized network architectures towards 5G-advanced and 6G era. Specifically, considering the scenario that multiple tasks randomly arrive at the network, the additional task queuing makes the delay analysis of coded computing more challenging. In this paper, we consider the impacts of task queuing and characterize the end-to-end delay for coded computing systems under the multi-task scenario. To this end, we first model the end-to-end coded computing system. Then, based on the redundant task processing strategies, we consider both purging and non-purging coded computing schemes. Although the expected end-to-end delay for both schemes are intractable, we obtain closed-form expressions for their respective lower and upper bounds, which generalizes the delay results of the single-task scenario. Moreover, we show that the multi-task coded computing has a coding gain of Θ(logn) wherendenotes the number of worker nodes, even with task queues considered. Simulation results verify the accuracy of the derived delay bounds and show the effectiveness of coded computing in the multi-task scenario.