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Analysis of DCTCP: stability, convergence, and fairness

Published: 07 June 2011 Publication History
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  • 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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    References

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      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
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      Published: 07 June 2011

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      Author Tags

      1. TCP
      2. analysis
      3. congestion control
      4. data center network

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      • (2023)Illuminating the hidden challenges of data-driven CDNsProceedings of the 3rd Workshop on Machine Learning and Systems10.1145/3578356.3592574(94-103)Online publication date: 8-May-2023
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