Jul 2026· IEEE Conference on Local Computer Networks· Vol abs/2607.26231· 0 citations· 32 references
Computer Science
TL;DR
ProFlow leverages distributed telemetry signals and offline-trained reinforcement learning to identify precursor congestion conditions and proactively reroute protected flows before throughput degradation occurs, demonstrating the effectiveness of anticipatory congestion management.
Abstract
In datacenter fabrics composed of leaf and aggregation switches, competing flows may become co-located on shared aggregation switches, creating congestion that can significantly degrade protected flows. However, before throughput degradation becomes observable, the network often exhibits early signs characterized by rising flow activity and queue overflow signals. Existing congestion-management approaches primarily react only after congestion becomes visible, leaving these early signs largely unexploited. In this paper, we propose ProFlow, a proactive flow-placement framework for protecting performance-sensitive traffic in multi-tenant datacenter networks, thereby utilizing the early signs of potential throughput degradations. ProFlow leverages distributed telemetry signals and offline-trained reinforcement learning (RL) to identify precursor congestion conditions and proactively reroute protected flows before throughput degradation occurs. Evaluation results using FABRIC testbed show that ProFlow achieves approximately 40% higher mean throughput than a reactive rerouting baseline while initiating rerouting decisions around 34 seconds earlier on average, demonstrating the effectiveness of anticipatory congestion management.
ProLet is a load balancing scheme that enables proactive probing and reroutes elephant flows at flowlet granularity in lossless RDMA networks and reduces average and tail flow completion time slowdowns by 69% and 79%, respectively, compared to state-of-the-art load balancing schemes.
Hong Wang, Jin-Hao Luo, J. Tan et al.· Asia-Pacific Workshop on Net...· 0 citations
Datacenter performance is often limited by network-centric congestion controls relying on low-level metrics (e.g., packet loss, latency) that misinterpret applications needs. This work argues that applications should participate in congestion control decisions and introduces the starvation ratio (SR), a metric that detects when applications are truly limited by the network. Experimental evaluations within an 11-flow bottleneck scenario on a Linux-based prototype show that asynchronous applications can absorb network variations within a newly identified “silence zone” without degradation, proving conventional controls are overly restrictive. By deploying proactive and reactive mechanisms, our approach consistently reduces Flow Completion Time (FCT) for network-sensitive workloads. Notably, the proactive configuration eliminates micro-recovery delays, keeping the starvation ratio close to zero and reinforcing the baseline protocol through stable congestion window regulation. We conclude that shifting to application-driven signaling aligns network transmission with the receiver’s processing pace, preventing computational underutilization.
Anderson Henrique da Silva Marcondes, Enzo B. Boscatto, Guilherme Piêgas Koslovski· Anais do LIII Seminário Inte...· 0 citations
In data centers, large-scale many-to-one traffic can rapidly exhaust switch buffers and trigger priority-based flow control (PFC) pause, resulting in increased flow completion time (FCT) for uncongested flows. To address this issue, we propose an innovative switch-side fast and accurate flow control (FAFC) scheme. By differentially allocating pause time for each port during congestion, FAFC can minimize the performance loss for uncongested flows. Furthermore, FAFC is also coupled with an effective queue length prediction algorithm to enable proactive and reliable estimation of the congestion level. Extensive system-level simulations demonstrate that FAFC can flexibly allocate pause times across congested ports, which are not only compatible with existing PFC but also do not require per-flow states. We implemented FAFC in P4 programmable switches, showing it as lightweight flow control method that is portable for implementation in hardware. Remarkably, our large-scale simulations illustrate that compared to traditional PFC, FAFC improves the average FCT slowdown and 95% FCT slowdown by 10.6% and 23.3%, respectively, under Hadoop workload when performing HPCC congestion control.
Chengdi Lu, Yuang Chen, Fangyu Zhang et al.· IEEE Transactions on Network...· 0 citations
Evaluation on representative workloads demonstrates that P2CS achieves performance comparable to in-network mechanisms while significantly reducing complexity and cost, and requires minimal software changes making it readily deployable in today's datacenter infrastructure.
Ali Munir, Xiaolin Pang, Junyi Zhang· Conference on Applications,...· 0 citations
Driven by the requirements of machine learning, cloud storage, and other network-intensive applications, remote direct memory access (RDMA) has been widely adopted in high-speed networks and is gradually being applied to geographically distributed datacenters. However, in cross-datacenter scenarios, long control loop latency and mixed traffic prevent existing RDMA congestion control schemes from perceiving and reacting to congestion in a timely and fair manner; this can lead to severe performance degradation and unfairness. To address these issues, we propose cdcPIM, a proactive congestion control scheme extended from datacenter parallel iterative matching (dcPIM) for cross-datacenter networks, which restructures the end-to-end control loop by introducing switch-coordinated control points, effectively transforming long-haul, RTT-bound feedback into localized control. Specifically, cdcPIM deploys a local control point by moving the token generation from the receiver to the sender side cross-datacenter switch, constraining the congestion control loop for inter-datacenter traffic within a single datacenter. Furthermore, cdcPIM introduces a remote control point to perform admission control for inter-datacenter traffic entering the receiver’s datacenter, thus avoiding intra-datacenter congestion caused by traffic bursts. Simulations demonstrate that when cdcPIM manages inter-datacenter traffic while cooperating with datacenter quantized congestion notification (DCQCN) for intra-datacenter traffic, long-haul congestion is effectively mitigated. Under mixed cross-datacenter workloads, DCQCN + cdcPIM reduces the overall average flow completion time (FCT) slowdown by up to 25.7% and the P99 FCT slowdown of intra-DC flows by up to 65.0% compared with the baseline scheme Themis.
Wenqiang Deng, Junyan Chen, Xuefeng Huang et al.· Journal of King Saud Univers...· 0 citations
Initial evaluations demonstrate that PFC-S can reduce the average flow completion time and effectively prevent congestion spreading, and experimental results show that PFC-S provides better protection for victim flows compared to standard PFC, BFC, and HPCC methods.
Weimin Gao, Jiawei Huang, Qile Wang et al.· Journal of High Speed Networ...· 0 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
