Skip to main content
Log in

Horizon: a QoS management framework for SDN-based data center networks

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

Data center networks (DCNs), which serve as the infrastructural backbone of big data, have been receiving increasing attention recently. To improve the service quality of data centers, researchers have been working on congestion control, network monitoring, and performance optimization. However, most such works focus on user-centric service quality, which means that the quality of network service itself is not factored into the quality of service (QoS) problem. In this study, we illustrate the problem of data center operations and management as a new type of QoS that is the foundation of user-centric QoS implementation. Inspired by traditional works on network performance optimization, we define the quality of network service in a software-defined networking (SDN)-based DCN and develop a framework called Horizon as the architecture of our QoS solution. This framework comprises a Markov-process-based method to predict link popularity, and we use SDN technology to monitor network status. We implement the proposed method, and the experimental results indicate that Horizon can relieve congestion in DCNs to meet QoS requirements. The experimental results show that our approach has a similar performance to the optimal solution. When compared with the ECMP approach, our approach has a much lower latency. The results also show that the proposed approach is effective in terms of network congestion control.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Stergiou C, Psannis KE, Kim B-G, Gupta B 2016 Secure integration of IoT and cloud computing. Futur Gener Comput Syst

  2. Alsmirat MA, Jararweh Y, Obaidat I, Gupta BB 2016 Internet of surveillance: a cloud supported large-scale wireless surveillance system. J Supercomput 1–20

  3. Dayarathna M, Wen Y, Fan R (2015) Data center energy consumption modeling: a survey. IEEE Commun Surv Tutorials 18(1):732–794

    Article  Google Scholar 

  4. Wei X-L, Chen M, Fan J-H, Zhang G-M, Lu Z-Y (2013) Architecture of the DCN. J Softw 24(2):295–316

    Article  Google Scholar 

  5. Guo C, Wu H, Tan K, Shi L, Zhang Y, Lu S (2008) DCell: a scalable and fault-tolerant network structure for data centers. Proc. ACM SIGCOMM 2008 Conf. Data Commun.–SIGCOMM ‘08, p. 75

  6. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28(5):755–768

    Article  Google Scholar 

  7. Chen K, Hu C, Zhang X, Zheng K, Chen Y, Vasilakos AV (2011) Survey on routing in data centers: insights and future directions. IEEE Netw 25(4):6–10

    Article  Google Scholar 

  8. Badve O, Gupta BB, Gupta S 2016 Reviewing the security features in contemporary security policies and models for multiple platforms. Handb Res Mod Cryptogr Solut Comput Cyber Secur 479–504

  9. Greenberg A, Lahiri P, Maltz DA, Patel P, Sengupta S 2008 Towards a next generation data center architecture: scalability and commoditization. ACM Work. Program. routers extensible Serv tomorrow 57–62

  10. Benson T, Anand A, Akella A, Zhang M (2010) Understanding data center traffic characteristics. SIGCOMM Comput Commun Rev 40(1):92–99

    Article  Google Scholar 

  11. Greenberg A, Hamilton JR, Jain N, Kandula S, Kim C, Lahiri P, Maltz DA, Patel P, and Sengupta S 2009 VL2: A Scalable and Flexible DCN. ACM SIGCOMM Conf Data Commun 51–62

  12. Al-Fares M, Loukissas A, Vahdat A (2008) A scalable, commodity DCN architecture. Sigcomm 38:63–74

    Article  Google Scholar 

  13. Kim J, Dally WJ, Abts D (2007) Flattened butterfly: a cost-efficient topology for high-radix networks. ISCA 35(2):126–137

    Article  Google Scholar 

  14. Nguyen K, Yamada S (2016) An experimental feasibility study on applying SDN technology to disaster-resilient wide area networks. Ann. des Telecommun. Telecommun. 71(11–12):639–647

    Article  Google Scholar 

  15. Lu Y (2016) SED: an SDN-based explicit-deadline-aware TCP for cloud data center networks. Tsinghua Sci Technol 21(5):491–499

    Article  Google Scholar 

  16. McKeown N, Anderson T, Balakrishnan H, Parulkar G, Peterson L, Rexford J, Shenker S, Turner J (2008) OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput Commun Rev 38(2):69

    Article  Google Scholar 

  17. Wallner R, Cannistra R (2013) An SDN approach: quality of service using big Switch’s Floodlight open-source controller. Proc Asia-Pacific Adv Netw 35:14

    Article  Google Scholar 

  18. Egilmez HE, Dane ST 2012 OpenQoS: An OpenFlow controller design for multimedia delivery with end-to-end quality of service over software-defined networks. Signal Inf. Process. Assoc. Annu. Summit Conf. (APSIPA ASC), pp. 1–8

  19. Xu G, Pang J, Fu X (2013) A load balancing model based on cloud partitioning for the public cloud. Tsinghua Sci Technol 18(1):34–39

    Article  MATH  Google Scholar 

  20. Jo E, Pan D, Liu J, Butler L (2015) A simulation and emulation study of SDN-based multipath routing for fat-tree DCNs. Proc - Winter Simul Conf 2015:3072–3083

    Google Scholar 

  21. Al-Fares M, Radhakrishnan S, and Raghavan B 2010 Hedera: Dynamic Flow Scheduling for DCNs. Nsdi 19

  22. Li J, Chang X, Ren Y, Zhang Z, Wang G 2015 An effective path load balancing mechanism based on SDNl. Proc. - 2014 I.E. 13th Int. Conf. Trust. Secur. Priv. Comput. Commun. Trust. 2014, pp 527–533

  23. Wu H, Feng Z, Guo C, Zhang Y (2013) ICTCP: Incast congestion control for TCP in data-center networks. IEEE/ACM Trans Netw 21(2):345–358

    Article  Google Scholar 

  24. Mallik A, Hegde S (2014) A novel proposal to effectively combine multipath data forwarding for data center networks with congestion control and load balancing using software-defined networking approach. 2014 Int Conf Recent Trends Inf Technol ICRTIT 201

  25. Hopps CE 2000 Analysis of an equal-cost multi-path algorithm status. RFC 2992 [Online]. Available: http://tools.ietf.org/html/rfc2992

  26. Alizadeh M, Atikoglu B, Kabbani A, Lakshmikantha A, Pan R, Prabhakar B, Seaman M 2008 Data center transport mechanisms: congestion control theory and IEEE standardization. 46th Annu Allert Conf Commun Control Comput pp 1270–1277

  27. Jun BAI, Jingbo XIA, Weihu Z, Jixiang WU2014 Weights evaluation of network links based on Markov chain model. J Chongqing Univ Posts Telecommun 26

  28. Wang Y, Bi J, Lin P, Lin Y, Zhang K (2016) SDI: a multi-domain SDN mechanism for fine-grained inter-domain routing. Ann des Telecommun Telecommun 71(11–12):625–637

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded by the following: National SCI-Tech Support Plan of China under Grant No. 2014BAH02F02, European Framework Program (FP7) under Grant no. FP7-PEOPLE-2011-IRSES, National Natural Science Foundation of China under Grants no. 61073009 and no. 61103197, National High Tech R&D Program 863 of China under Grant no. 2011AA010101, National SCI-Tech Support Plan of China under Grant no. 2014BAH02F03, National SCI-Tech Major Projects of China under Grant no. SinoProbe-09-01-03 & 2012ZX01039-004-04-3, Key SCI-Tech Program of Jilin Province of China under Grant no. 2011ZDGG007, the Key Scientific and Technological Project of Jilin Province of China under Grant no. 20150204035GX, and the Fundamental Research Funds for the Central Universities of China under Grant no. JCKY-QKJC46.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kuo Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pang, J., Xu, G., Fu, X. et al. Horizon: a QoS management framework for SDN-based data center networks. Ann. Telecommun. 72, 597–605 (2017). https://doi.org/10.1007/s12243-017-0579-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-017-0579-2

Keywords

Navigation