论文研读

Remote Direct Memory Access (RDMA) equips modern data centers (DCs) with high-performance data trans- mission. Yet, its potential is often unmet, as the State-Of-The-Art (SOTA) RDMA traffic load balancers struggle to balance high- performance with practicability. In this paper, we first analyse existing RDMA-aware load balancers, examining their trade-offs across four key metrics: path utilization, packet order preserva- tion, implementation overhead, and deployment complexity. We then present HP3, a host-based load balancer designed to excel in all these dimensions. HP3 combines Proactive Path Perception for real-time path state awareness with Reorder Free Rerouting to guarantee in-order packet delivery. It maintains low intrusiveness by complying with the standard RDMA transport layer and activating only during severe congestion. Implemented purely on end-hosts with no switch modifications, HP3 offers a practical solution with modest additional overhead. We prototype HP3 using DPDK and commodity RNICs, and evaluate it on a P4- programmable switch testbed. Experiments with diverse data center workloads show that HP3 outperforms SOTA RDMA load balancers by up to 3.4× for typical traffic and 1.3× for AI training traffic.

Next-generation datacenters require highly efficient network load balancing to manage the growing scale of artificial intelligence (AI) training and general datacenter traffic. However, existing Ethernet-based solutions, such as Equal Cost MultiPath (ECMP) and oblivious packet spraying (OPS), struggle to maintain high network utilization due to both increasing traffic demands and the expanding scale of datacenter topologies, which also exacerbate network failures. To address these limitations, we propose REPS, a lightweight decentralized per-packet adaptive load balancing algorithm designed to optimize network utilization while ensuring rapid recovery from link failures. REPS adapts to network conditions by caching good-performing paths. In case of a network failure, REPS re-routes traffic away from it in less than 100 microseconds. REPS is designed to be deployed with next-generation out-of-order transports, such as Ultra Ethernet, and uses less than 25 bytes of per-connection state regardless of the topology size. We extensively evaluate REPS in large-scale simulations and FPGA-based NICs.

Modern data centers (DCs) employ various traffic load balancers to achieve high bisection bandwidth. Among them, flowlet switching has shown remarkable performance in both load balancing and upper-layer protocol (e.g., TCP) friendliness. However, flowlet-based load balancers suffer from the inflexibility of flowlet timeout value (FTV) and result in sub-optimal performance under various application workloads. To this end, we propose Halflife, a novel flowletbased load balancer that leverages fading FTVs to reroute traffic promptly under different workloads without any prior knowledge. Halflife not only balances traffic better, but also avoids the performance degradation caused by frequent oscillation or shifting of flows between paths. Furthermore, Halflife’s fading mechanism is not only compatible with most flowlet-based load balancers, such as CONGA and LetFlow, but also improves their performance when leveraging flowlet switching in RDMA network. Through testbed experiments and simulations, we prove that Halflife improves the performance of CONGA and LetFlow by 10% ∼ 150%, and it outperforms other load balancers by 30% ∼ 200% across most application workloads.

RDMA is promising for enhancing the performance of cross-datacenter (DC) services. However, deploying RDMA over wide-area networks introduces severe congestion control unfairness, primarily due to asymmetric congestion feedback delays between inter-DC flows and intra-DC flows. As a result, intra-DC flows often bear the full burden of congestion response, leading to drastically increased flow completion times (FCT). In this work, we identify two key forms of unfairness — nearsource and near-destination — depending on whether congestion occurs near the sender or receiver of inter-DC flows. Based on this, we propose THEMIS, a fairness maintenance patch for long-haul RDMA networks. To mitigate near-source unfairness, THEMIS devises a Proactive Notification Point to shorten the congestion feedback loop within a single DC. To alleviate neardestination unfairness, THEMIS introduces a Temporary Reaction Point to temporarily slow down the target inter-DC flow until the sender receives the corresponding congestion feedback. We implement an open-source prototype of THEMIS, and evaluate it on both real-world testbed and large-scale simulations. Compared to DCQCN, Annulus and BiCC, THEMIS reduces the intra-DC FCT by up to 79.2%, 63.6% and 55.6%, and decreases overall FCT by up to 61.2%, 31.9% and 59.5% respectively.

Modern datacenter applications demand high throughput (40Gbps) and ultra-low latency 10 µs per hop from the network, with low CPU overhead. Standard TCP/IP stacks cannot meet these requirements, but Remote Direct Memory Access (RDMA) can. On IP-routed datacenter networks, RDMA is deployed using RoCEv2 protocol, which relies on Priority-based Flow Control (PFC) to enable a drop-free network. However, PFC can lead to poor application performance due to problems like head-of-line blocking and unfairness. To alleviates these problems, we introduce DCQCN, an end-to-end congestion control scheme for RoCEv2. To optimize DCQCN performance, we build a fluid model, and provide guidelines for tuning switch buffer thresholds, and other protocol parameters. Using a 3-tier Clos network testbed, we show that DCQCN dramatically improves throughput and fairness of RoCEv2 RDMA traffic. DCQCN is implemented in Mellanox NICs, and is being deployed in Microsoft’s datacenters.

Congestion control (CC) is the key to achieving ultra-low latency, high bandwidth and network stability in high-speed networks. From years of experience operating large-scale and high-speed RDMA networks, we find the existing high-speed CC schemes have inherent limitations for reaching these goals. In this paper, we present HPCC (High Precision Congestion Control), a new high-speed CC mechanism which achieves the three goals simultaneously. HPCC leverages in-network telemetry (INT) to obtain precise link load information and controls traffic precisely. By addressing challenges such as delayed INT information during congestion and overreaction to INT information, HPCC can quickly converge to utilize free bandwidth while avoiding congestion, and can maintain near-zero in-network queues for ultra-low latency. HPCC is also fair and easy to deploy in hardware. We implement HPCC with commodity programmable NICs and switches. In our evaluation, compared to DCQCN and TIMELY, HPCC shortens flow completion times by up to 95%, causing little congestion even under large-scale incasts.