PCM: Programmable Congestion Management for Terabit RDMA NICs

Mikhail Khalilov; Timo Schneider; Tommaso Bonato; Siyuan Shen; Marcin Chrapek; Yehor Kozyr; Alberto Merino; Rip Sohan; Surendra Anubolu; Mohan Kalkunte; Ahmad Ghalayini; Abdul Kabbani; Torsten Hoefler

arXiv · 2610.13130

PCM: Programmable Congestion Management for Terabit RDMA NICs

Mikhail Khalilov·Timo Schneider·Tommaso Bonato·Siyuan Shen·Marcin Chrapek·Yehor Kozyr·Alberto Merino·Rip Sohan·Surendra Anubolu·Mohan Kalkunte·Ahmad Ghalayini·Abdul Kabbani·Torsten Hoefler

View original source ↗Read source document ↗Export metadata ↓

Abstract

Datacenter congestion control (CC) and load balancing (LB) evolve rapidly as workload demands change, but deployment depends on RDMA hardware and transport interfaces. Emerging programmable RDMA engines tie policy code to vendor APIs. Operators must therefore port each policy for every platform they deploy. We address this with PCM, a programming model that separates portable congestion management from platform integration on datacenter NICs. Developers write event-driven handlers that they can debug in simulation and reuse on real hardware. PCM exposes flow telemetry as signals and uses triggers to support per-event reactions and coarser responses to accumulated feedback. Vendors provide optimized backends that map signals and output controls to native NIC mechanisms with defined feedback and control semantics. We demonstrate PCM's expressivity by implementing eleven well-known datacenter CC and LB algorithms. We deploy PCM across four distinct datapaths: an Ultra Ethernet simulator, CPU-based UCCL, and on-NIC offloads on NVIDIA ConnectX-8 and Microsoft 1P. On CX-8, PCM-Swift achieves processing capacity equivalent to one reaction per three 4 KiB packets at 800 Gbit/s, using one-fifth of the NIC's hardware threads. In UCCL, it matches native Swift's 1.56 Tbit/s goodput with 64 KiB chunk-level control. PCM's design informed ongoing Ultra Ethernet work toward a portable congestion management interface.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Mikhail Khalilov, Timo Schneider, Tommaso Bonato, Siyuan Shen, Marcin Chrapek, Yehor Kozyr, Alberto Merino, Rip Sohan, Surendra Anubolu, Mohan Kalkunte, Ahmad Ghalayini, Abdul Kabbani, Torsten Hoefler. 2026-10-09. PCM: Programmable Congestion Management for Terabit RDMA NICs. https://arxiv.org/abs/2610.13130

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistence

New Radio operating in unlicensed spectrum (NR-U) and Wi-Fi employ different contention parameters and channel-occupancy durations, which can produce severe coexistence imbalance. This correspondence develops a fairness-guided deep Q-network (DQN) that adjusts the NR-U transmission opportunity (TXOP) using the observed per-node throughput ratio. Two reward designs, Q1 and Q2, expose complementary operating regimes. Q1 promotes strict per-node throughput balance, whereas Q2 admits a broader inter-radio-access-technology fairness region and rewards throughput improvement in an intermediate fairness band. The policies are compared with the default Third Generation Partnership Project (3GPP) listen-before-talk (LBT), an adapted multi-armed-bandit method, a utility-fairness-based tabular Q-learning comparator, and a default-referenced fixed-TXOP baseline. Experiments across four priority classes and asymmetric populations report aggregate throughput, user-level Jain fairness, and concave throughput utility over 20 independent trials. The results characterize the cost of strict fairness, efficiency recovery under moderate relaxation, and transfer from symmetric to asymmetric deployments.

cs.NI↗

arXiv2026

Energy-Aware Receiver-initiated MAC Protocol for Energy Harvesting IoT

Energy harvesting (EH) technology has emerged as a key approach for extending the operational autonomy of Internet of Things (IoT) devices. In this context, medium access control (MAC) protocols for EH-IoT make use of the harvested energy efficiently to enhance the application performance. However, many existing MAC protocols rely on stored energy the battery in devising energy-aware strategies, which can interrupt the device operation and hinder the execution of ongoing tasks in dynamic and low EH conditions. To address this, this paper presents an energy-aware receiver-initiated MAC protocol for EH-IoT devices, called EARI-MAC, that adjusts the duty cycle of the receiver device based on stored energy and current harvesting power using the mathematical formulation. This approach supports sustained receiver operation under low EH conditions and improved application performance when harvested energy is abundant. The performance of the EARI-MAC has been evaluated in simulation using the real solar data gathered in Novi Sad, for three consecutive days. The simulation results demonstrate that the EARI-MAC utilizes the available energy resources efficiently and improves the packet delivery ratio and network throughput by more than 10%, reduces the receiver energy consumption by 5.6% and 8.2% and energy consumption per bit by 9.6% and 13.9% compared with QPPD-MAC and energy-unaware MAC, respectively.

cs.NI↗

arXiv2026

Configuration Migration Planning through Decoupled Modeling and Searching

Executing large network configuration changes, called migrations, is slow and error-prone today; operators must manually devise a safe order for updating devices that does not disrupt traffic and meets operational intents (e.g., low total time). We develop Smoothie, a versatile migration planning framework that, unlike existing tools, can support diverse operational intents. Inspired by automated planning, it achieves versatility by decoupling the encoding of network safety and intents from the search for a viable plan. Smoothie uses symbolic simulation for general and scalable safety encoding, after which it can use a range of planners, including existing off-the-shelf planners and a new one we developed for optimized parallel plans. We demonstrate Smoothie's versatility and benchmark its performance using diverse migration scenarios on many topologies. It finds both sequential plans and parallel plans, and the parallel ones compress up to thousands of configuration updates into no more than 3 steps. Its versatility does not come at the expense of performance: finishing most planning tasks in less than 1 hour.

cs.NI↗