research-article

Analysis of DCTCP: stability, convergence, and fairness

Authors:

Mohammad Alizadeh,

Adel Javanmard,

Balaji PrabhakarAuthors Info & Claims

SIGMETRICS '11: Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems

Pages 73 - 84

https://doi.org/10.1145/1993744.1993753

Published: 07 June 2011 Publication History

Abstract

Cloud computing, social networking and information networks (for search, news feeds, etc) are driving interest in the deployment of large data centers. TCP is the dominant Layer 3 transport protocol in these networks. However, the operating conditions---very high bandwidth links, low round-trip times, small-buffered switches---and traffic patterns cause TCP to perform very poorly. The Data Center TCP (DCTCP) algorithm has recently been proposed as a TCP variant for data centers and addresses these shortcomings.

In this paper, we provide a mathematical analysis of DCTCP. We develop a fluid model of DCTCP and use it to analyze the throughput and delay performance of the algorithm, as a function of the design parameters and of network conditions like link speeds, round-trip times and the number of active flows. Unlike fluid model representations of standard congestion control loops, the DCTCP fluid model exhibits limit cycle behavior. Therefore, it is not amenable to analysis by linearization around a fixed point and we undertake a direct analysis of the limit cycles, proving their stability. Using a hybrid (continuous- and discrete-time) model, we analyze the convergence of DCTCP sources to their fair share, obtaining an explicit characterization of the convergence rate. Finally, we investigate the "RTT-fairness" of DCTCP; i.e., the rate obtained by DCTCP sources as a function of their RTTs. We find a very simple change to DCTCP which is suggested by the fluid model and which significantly improves DCTCP's RTT-fairness. We corroborate our results with ns2 simulations.

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https://dl.acm.org/doi/10.1145/3651890.3672243
Gupta JKant KPal ABiswas J(2024)Configuring and Coordinating End-to-end QoS for Emerging Storage InfrastructureACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/36316069:1(1-32)Online publication date: 15-Jan-2024
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Index Terms

Analysis of DCTCP: stability, convergence, and fairness
1. Networks
  1. Network protocols

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Published In

cover image ACM Conferences

SIGMETRICS '11: Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems

June 2011

376 pages

ISBN:9781450308144

DOI:10.1145/1993744

General Chair:
Arif Merchant
Google, USA
,
Program Chairs:
Kimberly Keeton
HP Labs, USA
,
Dan Rubenstein
Columbia University, USA

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 June 2011

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SIGMETRICS '11: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

June 7 - 11, 2011

California, San Jose, USA

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Li CNasr-Esfahany AZhao KNoorbakhsh KGoyal PAlizadeh MAnderson TSekar VYu MSeneviratne AVeitch D(2024)m3: Accurate Flow-Level Performance Estimation using Machine LearningProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672243(813-827)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672243
Gupta JKant KPal ABiswas J(2024)Configuring and Coordinating End-to-end QoS for Emerging Storage InfrastructureACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/36316069:1(1-32)Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1145/3631606
Hu JZhou ZZhang J(2024)Lightweight Automatic ECN Tuning Based on Deep Reinforcement Learning With Ultra-Low Overhead in Datacenter NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2024.345059621:6(6398-6408)Online publication date: Dec-2024
https://doi.org/10.1109/TNSM.2024.3450596
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Lim HKim JCho IJang KBai WHan DFedorova ANarayanan DDi Luna GQuerzoni L(2023)FlexPass: A Case for Flexible Credit-based Transport for Datacenter NetworksProceedings of the Eighteenth European Conference on Computer Systems10.1145/3552326.3587453(606-622)Online publication date: 8-May-2023
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M. Abdelmoniem ABensaou B(2023)Enhancing TCP via Hysteresis Switching: Theoretical Analysis and Empirical EvaluationIEEE/ACM Transactions on Networking10.1109/TNET.2023.326256431:6(2614-2623)Online publication date: Dec-2023
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Zhao YQiao C(2023)Distributed Transport Protocols for Quantum Data NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2023.326254731:6(2777-2792)Online publication date: Dec-2023
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Du XXu KXu LZheng KShen MWu BLi T(2023)R-AQM: Reverse ACK Active Queue Management in Multitenant Data CentersIEEE/ACM Transactions on Networking10.1109/TNET.2022.319797331:2(526-541)Online publication date: Apr-2023
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Tang JXu TWang LLin ZZhao MWang XNguyen CTian CQian ZLi W(2023)SQCC: Stable Queue Congestion Control2023 14th International Conference on Network of the Future (NoF)10.1109/NoF58724.2023.10302751(19-27)Online publication date: 4-Oct-2023
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