Swift

Kumar, G. Santhosh; Dukkipati, Nandita; Jang, Keon; Wassel, Hassan M. G.; Wu, Xian; Montazeri, Behnam; Wang, Yaogong; Springborn, Kevin; Alfeld, Christopher; Ryan, Michael; Wetherall, David; Vahdat, Amin

doi:10.1145/3387514.3406591

Cited by 137 publications

(31 citation statements)

References 29 publications

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“…We recommend enabling selective ack (SACK), to avoid retransmitting packets that arrived out-of-order. Indeed, DCTCP [62] and Swift [70] enables SACK in data centers by default. On the other hand, we recommend using a TCP that does not react to duplicated ACKs (DACK) e.g., DCTCP [62], TCP BBR [71], Swift [70], etc.…”

Section: Dealing With Out-of-order Deliverymentioning

confidence: 99%

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Threshold-Based Routing-Topology Co-Design for Optical Data Center

Cao

Zhao

Dai

et al. 2023

IEEE/ACM Trans. Networking

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Despite the bandwidth scaling limit of electrical switching and the high cost of building Clos data center networks (DCNs), the adoption of optical DCNs is still limited. There are two reasons. First, existing optical DCN designs usually face high deployment complexity. Second, these designs are not full-optical and the performance benefit over the non-blocking Clos DCN is not clear. After exploring the design tradeoffs of the existing optical DCN designs, we propose TROD (Threshold Routing based Optical Datacenter), a low-complexity optical DCN with superior performance than other optical DCNs. There are two novel designs in TROD that contribute to its success. First, TROD performs robust topology optimization based on the recurring traffic patterns and thus does not need to react to every traffic change, which lowers deployment and management complexity. Second, TROD introduces tVLB (threshold-based Valiant Load Balance), which can avoid network congestion as much as possible even under unexpected traffic bursts. We conduct simulation based on both Facebook's real DCN traces and our synthesized highly bursty DCN traces. TROD reduces flow completion time (FCT) by about 1.15-2.16× compared to Google's Jupiter DCN, at least 2× compared to other optical DCN designs, and about 2.4-3.2× compared to expander graph DCN. Compared with the non-blocking Clos, TROD reduces the hop count of the majority packets by one, and could even outperform the non-blocking Clos with proper bandwidth over-provision at the optical layer. Note that TROD can be built with commercially available hardware and does not require host modifications.

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Section: Dealing With Out-of-order Deliverymentioning

confidence: 99%

“…Indeed, DCTCP [62] and Swift [70] enables SACK in data centers by default. On the other hand, we recommend using a TCP that does not react to duplicated ACKs (DACK) e.g., DCTCP [62], TCP BBR [71], Swift [70], etc. The reason is that packet reordering may not indicate a packet loss or network congestion under tVLB.…”

Section: Dealing With Out-of-order Deliverymentioning

confidence: 99%

Threshold-Based Routing-Topology Co-Design for Optical Data Center

Cao

Zhao

Dai

et al. 2023

IEEE/ACM Trans. Networking

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“…is version improves the coexistence with loss-based CCAs and reduces the loss rate of bottleneck buffer to less than 1.5BDP. At IETF-109 [38], Google proposed a new BBR.Swift algorithm based on the method of using delay as a congestion signal [39]. BBR.Swift can achieve higher fairness and lower retransmission rate when flows using the same CCA.…”

Section: Related Workmentioning

confidence: 99%

Improvement of BBRv2 Congestion Control Algorithm Based on Flow-aware ECN

Pan

Tan

et al. 2022

Security and Communication Networks

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Google proposed a new congestion control algorithm (CCA) based on bottleneck bandwidth and round-trip propagation time (BBR), which is considered to open a new era of congestion control. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. The BBR v2 algorithm is a recently updated version by Google, which aims to improve some of the problems in the original BBR (BBRv1) algorithm, such as interprotocol fairness issues, RTT fairness issues, and excessive retransmissions. The BBRv2 evaluation results show that it can improve the coexistence with the loss_based algorithm and alleviate some of the shortcomings in BBRv1. However, when multiple BBRv2 flows enter the same link at different times, fair convergence cannot be achieved, and RTT fairness still exists. Based on these problems, we analyze the root cause and proposed an improved algorithm BBRv2+, which uses flow-aware explicit congestion notification (ECN) to quantify queue information and feedback on the accurate congestion degree. BBRv2+ algorithm can avoid blind window constraints and selectively mark packets so that different flows can converge to fairness. In the simulation experiment of Network Simulator 3 (NS3), the results show that the BBRv2+ algorithm can improve intraprotocol fairness and RTT fairness and ensure bandwidth utilization and interprotocol fairness.

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“…In terms of transport layer latency, the authors of [ 21 ] report on experiences with a congestion control technique in Google datacenters. The scheme targets an end-to-end delay by using additive increase and multiplicative decrease in window size.…”

Section: Related Workmentioning

confidence: 99%

Delay Analysis in IoT Sensor Networks

Althoubi

Alshahrani

Peyravi

2021

Sensors

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Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model.

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Swift

Cited by 137 publications

References 29 publications

Threshold-Based Routing-Topology Co-Design for Optical Data Center

Threshold-Based Routing-Topology Co-Design for Optical Data Center

Improvement of BBRv2 Congestion Control Algorithm Based on Flow-aware ECN

Delay Analysis in IoT Sensor Networks

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