research-article

SOTE: : Traffic engineering in hybrid software defined networks

Authors:

H. Jonathan ChaoAuthors Info & Claims

Volume 154, Issue C

Pages 60 - 72

https://doi.org/10.1016/j.comnet.2019.03.008

Published: 08 May 2019 Publication History

Abstract

Traffic engineering (TE) is an efficient method to balance flows in a network. Existing TE solutions mainly focus on routing traffic along shortest paths between source and destination nodes, but ignore the likely network congestion. With the emergence of Software Defined Networking (SDN), through the deployment of SDN switches, we can enable flexible routing by splitting flows arbitrarily among multi-paths. However, due to technical, organizational and economical challenges of full SDN deployment, deploying SDN incrementally may be a more practical solution. Therefore, in this paper, we focus on a hybrid SDN architecture, where both legacy routers and SDN switches coexist. To explore routing optimization of TE in a hybrid SDN, we formulate a Mixed Integer Non-Linear programming (MINLP) problem and propose a heuristic algorithm SOTE to jointly optimize the OSPF weight setting and traffic splitting ratio of SDN nodes. Through extensive simulation and implementation on our prototyped testbed, we demonstrate that SOTE outperforms other algorithms in minimizing the maximum link utilization (MLU) of a network. Moreover, we also demonstrate that 10% deployment of SDN nodes can reap the most of benefit.

References

[1]

B. Fortz, M. Thorup, Optimizing OSPF/IS-IS weights in a changing world, IEEE J. Sel. Areas Commun. 20 (4) (2002).

[2]

B. Fortz, M. Thorup, Internet traffic engineering by optimizing OSPF weights, INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 2, IEEE, 2000, pp. 519–528.

[3]

M. Pióro, A. Szentesi, J. Harmatos, A. Jüttner, P. Gajowniczek, S. Kozdrowski, On open shortest path first related network optimisation problems, Perform. Eval. 48 (1) (2002) 201–223.

[4]

A. Sridharan, R. Guerin, C. Diot, Achieving near-optimal traffic engineering solutions for current OSPF/IS-IS networks, IEEE/ACM Trans. Netw. (TON) 13 (2) (2005) 234–247.

[5]

C.-Y. Hong, S. Kandula, R. Mahajan, M. Zhang, V. Gill, M. Nanduri, R. Wattenhofer, Achieving high utilization with software-driven wan, Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, ACM, 2013, pp. 15–26.

Digital Library

[6]

S. Jain, A. Kumar, S. Mandal, J. Ong, L. Poutievski, A. Singh, S. Venkata, J. Wanderer, J. Zhou, M. Zhu, et al., B4: experience with a globally-deployed software defined wan, Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, ACM, 2013, pp. 3–14.

Digital Library

[7]

S. Vissicchio, L. Vanbever, O. Bonaventure, Opportunities and research challenges of hybrid software defined networks, ACM SIGCOMM Comput. Commun. Rev. 44 (2) (2014) 70–75.

[8]

D. Xu, M. Chiang, J. Rexford, Deft: Distributed exponentially-weighted flow splitting, INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE, IEEE, 2007, pp. 71–79.

[9]

D. Xu, M. Chiang, J. Rexford, Link-state routing with hop-by-hop forwarding can achieve optimal traffic engineering, IEEE/ACM Trans. Netw. (TON) 19 (6) (2011) 1717–1730.

[10]

K. Xu, H. Liu, J. Liu, M. Shen, One more weight is enough: Toward the optimal traffic engineering with OSPF, Distributed Computing Systems (ICDCS), 2011 31st International Conference on, IEEE, 2011, pp. 836–846.

[11]

A. Elwalid, C. Jin, S. Low, I. Widjaja, Mate: MPLS adaptive traffic engineering, INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 3, IEEE, 2001, pp. 1300–1309.

[12]

S. Kandula, D. Katabi, B. Davie, A. Charny, Walking the tightrope: Responsive yet stable traffic engineering, ACM SIGCOMM Computer Communication Review, vol. 35, ACM, 2005, pp. 253–264.

Digital Library

[13]

M. Zhang, B. Liu, B. Zhang, Multi-commodity flow traffic engineering with hybrid MPLS/OSPF routing, Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, IEEE, 2009, pp. 1–6.

[14]

H. Zhang, W. Quan, H.-C. Chao, C. Qiao, Smart identifier network: a collaborative architecture for the future internet, IEEE Netw. 30 (3) (2016) 46–51.

Digital Library

[15]

W. Quan, N. Cheng, M. Qin, H. Zhang, H.A. Chan, X. Shen, Adaptive transmission control for software defined vehicular networks, IEEE Wireless Commun. Lett. (2018).

[16]

W. Quan, Y. Liu, H. Zhang, S. Yu, Enhancing crowd collaborations for software defined vehicular networks, IEEE Commun. Mag. 55 (8) (2017) 80–86.

[17]

S. Agarwal, S. Rajakrishnan, A. Narayan, R. Agarwal, D. Shmoys, A. Vahdat, Sincronia: near-optimal network design for coflows, Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, ACM, 2018, pp. 16–29.

[18]

M.M. Tajiki, B. Akbari, M. Shojafar, S.H. Ghasemi, M.L. Barazandeh, N. Mokari, L. Chiaraviglio, M. Zink, CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers, (2018) arXiv:1802.07840.

[19]

S. Agarwal, M. Kodialam, T. Lakshman, Traffic engineering in software defined networks, INFOCOM, 2013 Proceedings IEEE, IEEE, 2013, pp. 2211–2219.

[20]

Y. Hu, W. Wang, X. Gong, X. Que, Y. Ma, S. Cheng, Maximizing network utilization in hybrid software-defined networks, Global Communications Conference (GLOBECOM), 2015 IEEE, IEEE, 2015, pp. 1–6.

[21]

W. Wang, W. He, J. Su, Boosting the benefits of hybrid SDN, Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on, IEEE, 2017, pp. 2165–2170.

[22]

D.K. Hong, Y. Ma, S. Banerjee, Z.M. Mao, Incremental deployment of SDN in hybrid enterprise and ISP networks, Proceedings of the Symposium on SDN Research, ACM, 2016, p. 1.

[23]

M. Caria, T. Das, A. Jukan, M. Hoffmann, Divide and conquer: partitioning OSPF networks with SDN, IFIP/IEEE International Symposium on Integrated Network Management, 2015.

[24]

J. He, W. Song, Achieving near-optimal traffic engineering in hybrid software defined networks, IFIP Networking Conference, 2015.

[25]

H. Xu, J. Fan, J. Wu, C. Qiao, L. Huang, Joint deployment and routing in hybrid SDNs, Quality of Service (IWQoS), 2017 IEEE/ACM 25th International Symposium on, IEEE, 2017, pp. 1–10.

[26]

H. Xu, X.Y. Li, L. Huang, H. Deng, H. Huang, H. Wang, Incremental deployment and throughput maximization routing for a hybrid SDN, IEEE/ACM Trans. Netw. 25 (3) (2017) 1861–1875.

[27]

H. Xu, H. Huang, S. Chen, G. Zhao, Scalable software-defined networking through hybrid switching, INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, IEEE, 2017, pp. 1–9.

[28]

C.-Y. Chu, K. Xi, M. Luo, H.J. Chao, Congestion-aware single link failure recovery in hybrid SDN networks, Computer Communications (INFOCOM), 2015 IEEE Conference on, IEEE, 2015, pp. 1086–1094.

[29]

S. Vissicchio, L. Vanbever, J. Rexford, Sweet little lies: Fake topologies for flexible routing, Proceedings of the 13th ACM Workshop on Hot Topics in Networks, ACM, 2014, p. 3.

[30]

Y. Guo, Z. Wang, Y. Xia, X. Shi, J. Wu, Traffic engineering in hybrid SDN networks with multiple traffic matrices, Comput. Netw. 126 (2017).

[31]

N. Huin, M. Rifai, F. Giroire, D.L. Pacheco, G. Urvoy-Keller, J. Moulierac, Bringing energy aware routing closer to reality with SDN hybrid networks, IEEE Trans. Green Commun. Netw. (2018).

[32]

M.M. Tajiki, B. Akbari, N. Mokari, L. Chiaraviglio, SDN-based resource allocation in MPLS networks: a hybrid approach, Concurr. Comput. (2018) e4728.

[33]

Y. Guo, Z. Wang, X. Yin, X. Shi, J. Wu, Traffic engineering in SDN/OSPF hybrid network, Network Protocols (ICNP), 2014 IEEE 22nd International Conference on, IEEE, 2014, pp. 563–568.

[34]

N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, J. Turner, Openflow: enabling innovation in campus networks, ACM SIGCOMM Comput. Commun. Rev. 38 (2) (2008) 69–74.

Digital Library

[35]

M.R. Garey, D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, San Francisco, LA, 1979.

Digital Library

[36]

G. Cornuéjols, G.L. Nemhauser, L.A. Wolsey, The uncapacitated facility location problem, Technical Report, Carnegie-mellon univ pittsburgh pa management sciences research group, 1983.

[37]

B. Heller, R. Sherwood, N. Mckeown, The controller placement problem, ACM SIGCOMM Comput. Commun. Rev. 42 (4) (2012) 473–478.

[38]

P. Pheal, S. Panchen, N. McKee, InMon Corporations sFlow: A Method for Monitoring Traffic in Switched and Routed Networks, 1992.

[39]

I.I. CPLEX, V12. 1: Users manual for CPLEX, Int. Bus. Mach. Corp. 46 (53) (2009) 157.

[40]

G. Optimization, Inc., Gurobi Optimizer Reference Manual, Google Scholar, 2014.

[41]

B. Fortz, M. Thorup, Increasing internet capacity using local search, Comput. Optim. Appl. 29 (1) (2004) 13–48.

[42]

R. Hartert, S. Vissicchio, P. Schaus, O. Bonaventure, C. Filsfils, T. Telkamp, P. Francois, A declarative and expressive approach to control forwarding paths in carrier-grade networks, ACM SIGCOMM Computer Communication Review, vol. 45, ACM, 2015, pp. 15–28.

[43]

R. Mahajan, N. Spring, D. Wetherall, T. Anderson, Inferring link weights using end-to-end measurements, Proceedings of the 2nd ACM SIGCOMM Workshop on Internet Measurement, ACM, 2002, pp. 231–236.

[44]

S. Balon, G. Monfort, The traffic matrices and topology of the abilene network, (http://totem.run.montefiore.ulg.ac.be/datatools.html).

[45]

B. Zhang, J. Bi, J. Wu, F. Baker, CTE: cost-effective intra-domain traffic engineering, ACM SIGCOMM Computer Communication Review, vol. 44, ACM, 2014, pp. 115–116.

[46]

S. Uhlig, B. Quoitin, J. Lepropre, S. Balon, Providing public intradomain traffic matrices to the research community, ACM SIGCOMM Comput. Commun. Rev. 36 (1) (2006) 83–86.

[47]

N. Kang, M. Ghobadi, J. Reumann, A. Shraer, J. Rexford, Efficient traffic splitting on commodity switches, The ACM Conference, 2015, pp. 1–13.

Cited By

Ding MGuo YHuang ZLin BLuo H(2024)GROMJournal of Network and Computer Applications10.1016/j.jnca.2024.103927229:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.jnca.2024.103927
Pei XSun PHu YLi DChen BTian L(2024)Enabling efficient routing for traffic engineering in SDN with Deep Reinforcement LearningComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110220241:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110220

Index Terms

SOTE: Traffic engineering in hybrid software defined networks
1. Mathematics of computing
  1. Mathematical analysis

Index terms have been assigned to the content through auto-classification.

Recommendations

Routing optimization for IP networks with loop-free alternates

Loop-free alternates (LFAs) have been developed for fast reroute (FRR) in intradomain IP networks. They are simple, standardized, and already offered by several vendors. However, LFAs have two major drawbacks. They often cannot provide failure ...
Modified smell detection algorithm for optimal paths engineering in hybrid SDN
Abstract
Optimal path engineering in hybrid software defined networking environments involves leveraging traditional networking and software defined capabilities to determine the most efficient paths for network traffic. In hybrid software defined ...
Highlights
- Improve traffic management in existing WAN infrastructures by integrating a modified SDN Controller.
- Enable gradual SDN deployment to implement traffic engineering in legacy networks.
- SDA optimization in the SDN Controller enhances ...
DTARP: A Dynamic Traffic Aware Routing Protocol for Wireless Sensor Networks
RealWSN'18: Proceedings of the 7th International Workshop on Real-World Embedded Wireless Systems and Networks

The challenge of efficient point-to-point communication in meshed Wireless Sensor and Actuator Networks (WSANs) can be addressed by Software Defined Networking (SDN). In contrast to the traditional routing protocols for Wireless Sensor Networks (WSNs) ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 154, Issue C

May 2019

105 pages

ISSN:1389-1286

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 08 May 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ding MGuo YHuang ZLin BLuo H(2024)GROMJournal of Network and Computer Applications10.1016/j.jnca.2024.103927229:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.jnca.2024.103927
Pei XSun PHu YLi DChen BTian L(2024)Enabling efficient routing for traffic engineering in SDN with Deep Reinforcement LearningComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2024.110220241:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.comnet.2024.110220

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents