CCRP: Converging Credit-Based and Reactive Protocols in Datacenters - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2021

CCRP: Converging Credit-Based and Reactive Protocols in Datacenters

Yang Bai
  • Fonction : Auteur
  • PersonId : 1161023
Dinghuang Hu
  • Fonction : Auteur
  • PersonId : 1161024
Dezun Dong
  • Fonction : Auteur
  • PersonId : 1161025
Shan Huang
  • Fonction : Auteur
  • PersonId : 1161026
Xiangke Liao
  • Fonction : Auteur
  • PersonId : 1161027

Résumé

As the link speed has grown steadily from 10 Gbps to 100 Gbps, high-speed data center networks (DCNs) require more efficient congestion management. Therefore, proactive transports, especially credit-based congestion control, nowadays have drawn much attention because of fast convergence, near-zero queueing and low latency. However, in real deployment scenarios, it is hard to guarantee one protocol to be deployed in every host at one time. Thus, when the credit-based protocols are deployed into DCNs incrementally, the network will convert to multi-protocol state and face the following fundamental challenges: (i) unfairness, (ii) non-convergence, and (iii) high buffer occupancy. In this paper, we propose a new protocol, called CCRP, aiming for converging credit-based and reactive protocols in data centers. Targeting the mostly deployed protocol, i.e. DCQCN based on explicit congestion notification (ECN), in DCNs, CCRP leverages the forward ECN to detect the network congestion in data queue and optimizes feedback control of the credit-based transports. Our experiment results show that this design can address the unfair link allocation and converge with reactive protocols rapidly. Furthermore, CCRP achieves high utilization and low buffer occupancy at the same time.
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hal-03768736 , version 1 (04-09-2022)

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Yang Bai, Dinghuang Hu, Dezun Dong, Shan Huang, Xiangke Liao. CCRP: Converging Credit-Based and Reactive Protocols in Datacenters. 17th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2020, Zhengzhou, China. pp.420-434, ⟨10.1007/978-3-030-79478-1_36⟩. ⟨hal-03768736⟩
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