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Modeling and Analysis of the Latency-Based Congestion Control Algorithm DX

  • Wanchun JiangEmail author
  • Lijuan Peng
  • Chang Ruan
  • Jia Wu
  • Jianxin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)

Abstract

Nowadays, low latency has become one of the primary goals of congestion control in data center networks. To achieve low latency, many congestion control algorithms have been proposed, wherein DX is the first latency-based one. Specifically, DX tackles the accurate latency measurement problem, reduces the flow completion time and outperforms the de facto DCTCP algorithm significantly in term of median queueing delay. Although the advantages of DX have been confirmed by experimental results, the behaviors of DX have not been fully revealed. Accordingly, some drawbacks of DX under special environment are unexplored. Therefore, in this paper, we conduct fluid-flow analysis over DX, deducing sufficient condition for the stability of DX and revealing the behaviors of DX. Analytical results uncover two problems of DX: (1) it has poor throughput when either the base RTT is very large or the number of flows is relatively small; (2) it suffers from large queueing delay when either the base RTT is relatively small or the number of flows is very large. These results are instructive to the improvement and deployment of DX. Simulation results based on NS-3 verify our analytical results.

Keywords

Congestion control Fluid-flow analysis Stability Latency 

References

  1. 1.
    Alizadeh, M., et al.: Data center TCP (DCTCP). ACM SIGCOMM Comput. Commun. Rev. 40, 63–74 (2010)CrossRefGoogle Scholar
  2. 2.
    Alizadeh, M., Javanmard, A., Prabhakar, B.: Analysis of DCTCP: stability, convergence, and fairness. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp. 73–84. ACM (2011)Google Scholar
  3. 3.
    Alizadeh, M., Kabbani, A., Atikoglu, B., Prabhakar, B.: Stability analysis of QCN: the averaging principle. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp. 49–60. ACM (2011)Google Scholar
  4. 4.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  5. 5.
    Gao, P.X., Narayan, A., Kumar, G., Agarwal, R., Ratnasamy, S., Shenker, S.: pHost: distributed near-optimal datacenter transport over commodity network fabric. In: ACM Conference on Emerging Networking Experiments & Technologies (2015)Google Scholar
  6. 6.
    Golnaraghi, F., Kuo, B.: Automatic control systems. Complex Variables 2, 1–1 (2010)Google Scholar
  7. 7.
    Hollot, C.V., Misra, V., Towsley, D., Gong, W.: Analysis and design of controllers for AQM routers supporting TCP flows. IEEE Trans. Autom. Control 47(6), 945–959 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jiang, W., Ren, F., Shu, R., Wu, Y., Lin, C.: Sliding mode congestion control for data center ethernet networks. IEEE Trans. Comput. 64(9), 2675–2690 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lee, C., Park, C.: Accurate latency-based congestion feedback for datacenters. In: USENIX ATC, pp. 403–415 (2015)Google Scholar
  10. 10.
    Lee, C., Park, C., Jang, K., Moon, S., Han, D.: DX: latency-based congestion control for datacenters. IEEE/ACM Trans. Networking 25(1), 335–348 (2017)CrossRefGoogle Scholar
  11. 11.
    Misra, V., Gong, W.B., Towsley, D.: Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to red. ACM SIGCOMM Comput. Commun. Rev. 30, 151–160 (2000)CrossRefGoogle Scholar
  12. 12.
    Mittal, R., et al.: TIMELY: RTT-based congestion control for the datacenter. ACM SIGCOMM Comput. Commun. Rev. 45, 537–550 (2015)CrossRefGoogle Scholar
  13. 13.
    Srikant, R.: The Mathematics of Internet Congestion Control. Springer, New York (2012)zbMATHGoogle Scholar
  14. 14.
    Zhu, Y., Ghobadi, M., Misra, V., Padhye, J.: ECN or Delay: lessons learnt from analysis of DCQCN and TIMELY. In: Proceedings of the 12th International on Conference on Emerging Networking Experiments and Technologies, pp. 313–327. ACM (2016)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Wanchun Jiang
    • 1
    Email author
  • Lijuan Peng
    • 1
  • Chang Ruan
    • 1
  • Jia Wu
    • 1
  • Jianxin Wang
    • 1
  1. 1.School of Computer Science and EngineeringCenter South UniversityChangshaChina

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