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Journal of Computer Science and Technology

, Volume 33, Issue 6, pp 1152–1163 | Cite as

More Requests, Less Cost: Uncertain Inter-Datacenter Traffic Transmission with Multi-Tier Pricing

  • Xiao-Dong Dong
  • Sheng Chen
  • Lai-Ping Zhao
  • Xiao-Bo ZhouEmail author
  • Heng Qi
  • Ke-Qiu Li
Regular Paper
  • 36 Downloads

Abstract

With the multi-tier pricing scheme provided by most of the cloud service providers (CSPs), the cloud users typically select a high enough transmission service level to ensure the quality of service (QoS), due to the severe penalty of missing the transmission deadline. This leads to the so-called over-provisioning problem, which increases the transmission cost of the cloud user. Given the fact that cloud users may not be aware of their traffic demand before accessing the network, the over-provisioning problem becomes more serious. In this paper, we investigate how to reduce the transmission cost from the perspective of cloud users, especially when they are not aware of their traffic demand before the transmission deadline. The key idea is to split a long-term transmission request into several short ones. By selecting the most suitable transmission service level for each short-term request, a cost-efficient inter-datacenter transmission service level selection framework is obtained. We further formulate the transmission service level selection problem as a linear programming problem and resolve it in an on-line style with Lyapunov optimization. We evaluate the proposed approach with real traffic data. The experimental results show that our method can reduce the transmission cost by up to 65.04%.

Keywords

traffic uncertainty inter-datacenter transmission multi-tier pricing scheme 

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References

  1. [1]
    Anania L, Solomon R. Flat: The minimalist BISDN rate. University of Michigan Library Journal of Electronic Publishing, 1995, 1(2): 1-20.Google Scholar
  2. [2]
    Xu H, Li B. Joint request mapping and response routing for geo-distributed cloud services. In Proc. the 32nd IEEE INFOCOM Conference, April 2013, pp.854-862.Google Scholar
  3. [3]
    Dai W, Jordan S. ISP service tier design. IEEE/ACM Trans. Networking, 2016, 24(3): 1434-1447.CrossRefGoogle Scholar
  4. [4]
    Laoutaris N, Sirivianos M, Yang X, Rodriguez P. Interdatacenter bulk transfers with NetStitcher. In Proc. ACM SIGCOMM 2011 Conference, August 2011, pp.74-85.Google Scholar
  5. [5]
    Kandula S, Menache I, Schwartz R, Babbula S. Calendaring for wide area networks. In Proc. ACM SIGCOMM 2014 Conference, August 2014, pp.515-526.Google Scholar
  6. [6]
    Spang B, Sabnis A, Sitaraman R, Towsley D, De-Cleene B. MON: Mission-optimized overlay networks. In Proc. the 36th IEEE INFOCOM Conference, May 2017.Google Scholar
  7. [7]
    Zhang H, Chen K, Bai W, Han D, Tian C, Wang H, Guan H, Zhang M. Guaranteeing deadlines for inter-data center transfers. IEEE/ACM Trans. Networking, 2017, 25(1): 579-595.CrossRefGoogle Scholar
  8. [8]
    Jalaparti V, Bliznets I, Kandula S, Lucier B, Menache I. Dynamic pricing and traffic engineering for timely inter-datacenter transfers. In Proc. ACM SIGCOMM 2016 Conference, August 2016, pp.73-86.Google Scholar
  9. [9]
    Li W, Zhou X, Li K, Qi H, Guo D. More peak, less differentiation: Towards a pricing-aware online control framework for inter-datacenter transfers. In Proc. the 37th IEEE Int. Conference on Distributed Computing Systems, June 2017, pp.2105-2110.Google Scholar
  10. [10]
    Golubchik L, Khuller S, Mukherjee K, Yao Y. To send or not to send: Reducing the cost of data transmission. In Proc. the 32nd IEEE INFOCOM Conference 2013, April 2013, pp.2472-2478.Google Scholar
  11. [11]
    Divakaran D, Gurusamy M. Towards exible guarantees in clouds: Adaptive bandwidth allocation and pricing. IEEE Trans. Parallel Distributed System, 2015, 26(6): 1754-1764.CrossRefGoogle Scholar
  12. [12]
    Tang S, Yuan J, Li X. Towards optimal bidding strategy for Amazon EC2 cloud spot instance. In Proc. the 5th IEEE International Conference on Cloud Computing, June 2012, pp.91-98.Google Scholar
  13. [13]
    Yang S, Kuipers F. Traffic uncertainty models in network planning. IEEE Communications Magazine, 2014, 52(2): 172-177.CrossRefGoogle Scholar
  14. [14]
    Aparicio-Pardo R, Pavón-Mariño P, Mukherjee B. Robust upgrade in optical networks under traffic uncertainty. In Proc. the 16th International Conference on Optical Network Design and Modelling, April 2012.Google Scholar
  15. [15]
    Chen F, Wu C, Hong X, Lu Z, Wang Z, Lin C. Engineering traffic uncertainty in the openflow data plane. In Proc. the 35th IEEE INFOCOM Conference, April 2016.Google Scholar
  16. [16]
    Mitra D,Wang Q. Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management. IEEE/ACM Trans. Networking, 2005, 13(2): 221-233.CrossRefGoogle Scholar
  17. [17]
    Alizadeh M, Yang S, Sharif M, Katti S, McKeown N, Prabhakar B, Shenker S. pFabric: Minimal near-optimal datacenter transport. In Proc. ACM SIGCOMM 2013 Conference, August 2013, pp.435-446.CrossRefGoogle Scholar
  18. [18]
    Bai W, Chen K, Wang H, Chen L, Han D, Tian C. Information-agnostic flow scheduling for commodity data centers. In Proc. the 12th USENIX Symposium on Networked Systems Design and Implementation, May 2015, pp.455-468.Google Scholar
  19. [19]
    Wang T, Xu H, Liu F. Aemon: Information-agnostic mixflow scheduling in data center networks. In Proc. the 1st Asia-Pacific Workshop on Networking, APNet, August 2017, pp.106-112.Google Scholar
  20. [20]
    Jin X, Li Y, Wei D, Li S, Gao J, Xu L, Li G, Xu W, Rexford J. Optimizing bulk transfers with software-defined optical-WAN. In Proc. ACM SIGCOMM 2016 Conference, August 2016, pp.87-100.Google Scholar
  21. [21]
    Noormohammadpour M, Raghavendra C, Rao S. Dcroute: Speeding up inter-datacenter traffic allocation while guaranteeing deadlines. In Proc. the 23rd IEEE International Conference on High Performance Computing, December 2016, pp.82-90.Google Scholar
  22. [22]
    Lin Y, Shen H, Chen L. Ecoflow: An economical and deadline-driven inter-datacenter video flow scheduling system. In Proc. the 23rd ACM Conference on Multimedia Conference, October 2015, pp.1059-1062.Google Scholar
  23. [23]
    Hong C, Caesar M, Godfrey B. Finishing flows quickly with preemptive scheduling. In Proc. ACM SIGCOMM 2012 Conference, August 2012, pp.127-138.CrossRefGoogle Scholar
  24. [24]
    Munir A, Baig G, Irteza S, Qazi I, Liu A, Dogar F. Friends, not foes: Synthesizing existing transport strategies for data center networks. In Proc. ACM SIGCOMM 2014 Conference, August 2014, pp.491-502.Google Scholar
  25. [25]
    Chen L, Chen K, Bai W, Alizadeh M. Scheduling mix-flows in commodity datacenters with Karuna. In Proc. ACM SIGCOMM 2016 Conference, August 2016, pp.174-187.Google Scholar
  26. [26]
    Valancius V, Lumezanu C, Feamster N, Johari R, Vazirani V. How many tiers?: Pricing in the Internet transit market. In Proc. ACM SIGCOMM 2011 Conference, August 2011, pp.194-205.Google Scholar
  27. [27]
    Li S, Huang J. Price differentiation for communication networks. IEEE/ACM Trans. Networking, 2014, 22(3): 703-716.CrossRefGoogle Scholar
  28. [28]
    Xu H, Li B. Spot transit: Cheaper Internet transit for elastic traffic. IEEE Trans. Services Computing, 2015, 8(5): 768-781.MathSciNetCrossRefGoogle Scholar
  29. [29]
    Paxson V, Floyd S. Wide-area traffic: The failure of Poisson modeling. In Proc. ACM SIGCOMM 1994 Conference, September 1994, pp.257-268.CrossRefGoogle Scholar
  30. [30]
    Kopetz H, Ochsenreiter W. Clock synchronization in distributed real-time systems. IEEE Trans. Computers, 1987, 36(8): 933-940.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xiao-Dong Dong
    • 1
  • Sheng Chen
    • 1
  • Lai-Ping Zhao
    • 1
  • Xiao-Bo Zhou
    • 1
    Email author
  • Heng Qi
    • 2
  • Ke-Qiu Li
    • 1
  1. 1.Tianjin Key Laboratory of Advanced Networking, College of Intelligence and ComputingTianjin UniversityTianjinChina
  2. 2.School of Computer Science and TechnologyDalian University of TechnologyDalianChina

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