In this paper, we presentRoCC, a robust congestion control approach for datacenter networks based on RDMA.RoCCleverages switch queue size as an input to a PI controller, which computes the fair data rate of flows in the queue. The PI parameters are self-tuning to guarantee stability, rapid convergence, and fair and near-optimal throughput in a wide range of congestion scenarios. Our simulation and DPDK implementation results show thatRoCCcan achieve up to$7\times$reduction in PFC frames generated under high load levels, compared to DCQCN. At the same time,RoCCcan achieve$1.7 - 4.5\times$and$1.4 - 3.9\times$lower tail latency for long flows and$2.1-7\times$and$3.5-8.2\times$lower tail latency for short flows, compared to DCQCN and HPCC, respectively. We also find thatRoCCdoes not require PFC. The functional components ofRoCCcan be efficiently implemented in P4 and FPGA-based switch hardware.

In this paper, we present RoCC, a robust congestion control approach for datacenter networks based on RDMA. RoCC leverages switch queue size as an input to a PI controller, which computes the fair data rate of flows in the queue, signaling it to the flow sources. The PI parameters are self-tuning to guarantee stability, rapid convergence, and fair and near-optimal throughput in a wide range of congestion scenarios. Our simulation and DPDK implementation results show that RoCC can achieve up to 7× reduction in PFC frames generated under high average load levels, compared to DCQCN. At the same time, RoCC can achieve up to 8× lower tail latency, compared to DCQCN and HPCC. We also find that RoCC does not require PFC. The functional components of RoCC are implementable in P4-based and fixed-function switch ASICs.

We present dRMT (disaggregated Reconfigurable Match-Action Table), a new architecture for programmable switches. dRMT overcomes two important restrictions of RMT, the predominant pipeline-based architecture for programmable switches: (1) table memory is local to an RMT pipeline stage, implying that memory not used by one stage cannot be reclaimed by another, and (2) RMT is hardwired to always sequentially execute matches followed by actions as packets traverse pipeline stages. We show that these restrictions make it difficult to execute programs efficiently on RMT.

Switches today provide a small menu of scheduling algorithms. While we can tweak scheduling parameters, we cannot modify algorithmic logic, or add a completely new algorithm, after the switch has been designed. This paper presents a design for a {\em programmable} packet scheduler, which allows scheduling algorithms---potentially algorithms that are unknown today---to be programmed into a switch without requiring hardware redesign.

Packet scheduling in switches is not programmable; operators only choose among a handful of scheduling algorithms implemented by the manufacturer. In contrast, other switch functions such as packet parsing and header processing are becoming programmable [10, 3, 6]. This paper presents a programmable packet scheduler that allows operators to program a variety of scheduling algorithms.

We present the design, implementation, and evaluation of CONGA, a network-based distributed congestion-aware load balancing mechanism for datacenters. CONGA exploits recent trends including the use of regular Clos topologies and overlays for network virtualization. It splits TCP flows into flowlets, estimates real-time congestion on fabric paths, and allocates flowlets to paths based on feedback from remote switches. This enables CONGA to efficiently balance load and seamlessly handle asymmetry, without requiring any TCP modifications. CONGA has been implemented in custom ASICs as part of a new datacenter fabric. In testbed experiments, CONGA has 5x better flow completion times than ECMP even with a single link failure and achieves 2-8x better throughput than MPTCP in Incast scenarios. Further, the Price of Anarchy for CONGA is provably small in Leaf-Spine topologies; hence CONGA is nearly as effective as a centralized scheduler while being able to react to congestion in microseconds. Our main thesis is that datacenter fabric load balancing is best done in the network, and requires global schemes such as CONGA to handle asymmetry.