Improving Congestion Control Convergence in RDMA Networks

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2022

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Abstract

Remote Direct Memory Access (RDMA) networks are becoming a popular interconnect technology to enable high performance communication in distributed systems. While RDMA hardware enables high bandwidth and low latency networking, networks require congestion control algorithms to ensure they operate efficiently. Ideally, a congestion control algorithm allows computers in the network to inject enough traffic to keep the network fully utilized, stops computers from causing congestion, and allocates bandwidth fairly to all computers in the network. This enables the network to operate at peak performance and be fair to all users. While many protocols eventually converge to this ideal network state over time, they often take too long, reducing performance. We develop mechanisms and protocols that improve convergence time.

In this thesis, we identify several ways in which slow convergence to the ideal network state harms performance and leaves RDMA networks susceptible to performance isolation attacks. We show that slow convergence to fair injection rates on end-hosts in RDMA networks greatly increases the communication time for long flows, which leads to sub-optimal application performance. We identify why unfairness occurs and measure unfairness' impact on application performance. We then show that because RDMA networks are loss-less and start sending packet at line-rate, users can unfairly gain more bandwidth and sometimes ignore congestion control altogether. This allows misbehaving users to harm the performance of other users sharing the network.

To improve long flow performance in RDMA networks, we propose two new mechanisms for Additive-Increase Multiplicative-Decrease protocols: 1) Variable Additive Increase and 2) Sampling Frequency. These mechanisms reduce the time it takes for the network to allocate bandwidth fairly between end-hosts, so long flows are not starved of bandwidth. To create these mechanisms, we determine when unfairness occurs and how end-hosts can infer unfairness without any additional information from switches.

We then introduce One Round Trip Time Convergence (1RC) and a new method of setting flow weights, which improve performance and isolation in RDMA networks. 1RC enables a network to converge to fair rates during the first RTT by dropping packets when a flow uses too much bandwidth. We do this while maintaining packet ordering and fairness between flows that start sending packets at the same time. We then use a new weighting scheme, which decreases the bandwidth allocation to users opening too many connections. A lower weight for misbehaving users mitigates the impact of a user trying to gain more bandwidth than is fair.

Finally, we introduce the Collective Congestion Control Protocol (3CPO), which improves convergence in multicast networks designed for collective communication. Multicast operations send a single packet to several destinations at once and cause severe congestion in the network quickly. 3CPO manages multicast congestion by inferring global congestion state through multicast operations and then tunes each end-host injection rate to be near optimal. 3CPO requires no extra information from switches and works entirely on end-hosts.

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Snyder, John (2022). Improving Congestion Control Convergence in RDMA Networks. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/25289.

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