DOI QR코드

DOI QR Code

Reliability-aware service chaining mapping in NFV-enabled networks

  • Liu, Yicen (Shijiazhuang Campus, Army Engineering University) ;
  • Lu, Yu (Shijiazhuang Campus, Army Engineering University) ;
  • Qiao, Wenxin (Shijiazhuang Campus, Army Engineering University) ;
  • Chen, Xingkai (Shijiazhuang Campus, Army Engineering University)
  • Received : 2018.04.24
  • Accepted : 2018.09.05
  • Published : 2019.04.07

Abstract

Network function virtualization can significantly improve the flexibility and effectiveness of network appliances via a mapping process called service function chaining. However, the failure of any single virtualized network function causes the breakdown of the entire chain, which results in resource wastage, delays, and significant data loss. Redundancy can be used to protect network appliances; however, when failures occur, it may significantly degrade network efficiency. In addition, it is difficult to efficiently map the primary and backups to optimize the management cost and service reliability without violating the capacity, delay, and reliability constraints, which is referred to as the reliability-aware service chaining mapping problem. In this paper, a mixed integer linear programming formulation is provided to address this problem along with a novel online algorithm that adopts the joint protection redundancy model and novel backup selection scheme. The results show that the proposed algorithm can significantly improve the request acceptance ratio and reduce the consumption of physical resources compared to existing backup algorithms.

Keywords

References

  1. Cisco, Cisco visual networking index: forecast and methodology, 2016-2021, Sept. 2017.
  2. D. Bhamare, R. Jain, and M. Samaka, A survey on service function chaining, J. Netw. Comput. Applicat. 75 (2016), no. 3, 138-155. https://doi.org/10.1016/j.jnca.2016.09.001
  3. M. Mechtri, C. Ghribi, and D. Zeghlache, A scalable algorithm for the placement of service function chains, IEEE Trans. Netw. Service Manag. 13 (2016), no. 3, 533-546. https://doi.org/10.1109/TNSM.2016.2598068
  4. N. McKeown, T. Anderson, and H. Balakrishnan, Openflow: enabling innovation in campus networks, ACM SIGCOMM Comput. Commun. Rev. 38 (2008), no. 2, 69-75. https://doi.org/10.1145/1355734.1355746
  5. R. Mijumbi, J. Serrat, and J. L. Gorricho, Management and orchestration challenges in network functions virtualization, IEEE Commun. Mag. 54 (2016), no. 1, 98-105. https://doi.org/10.1109/MCOM.2016.7378433
  6. Y. Li and M. Chen, Software‐defined network function virtualization: a survey, IEEE Access 3 (2017), 2542-2553. https://doi.org/10.1109/ACCESS.2015.2499271
  7. S. Herker et al., Data-center architecture impacts on virtualized network functions service chain embedding with high availability requirements, IEEE GLOBECOM Workshops, San Diego, CA, Dec. 6-10, 2016, pp. 1-7.
  8. S. Ayoubi, Y. Zhang, and C. Assi, RAS: reliable auto-scaling of virtual machines in multi-tenant cloud networks, IEEE Int. Conf. Cloud Netw., Niagara Falls, Canada, Oct. 5-7, 2015, pp. 1-6.
  9. R. Guerzoni et al., Modeling reliability requirements in coordinated node and link mapping, IEEE Int. Symp. Reliable Distrib. Syst., Nara, Japan, Oct. 6-9, 2014, pp. 321-330.
  10. J. Sherry et al., Rollback‐recovery for middleboxes, ACM SIGCOMM Comput. Commun. Rev. 45 (2015), no. 4, 227-240. https://doi.org/10.1145/2829988.2787501
  11. Q. Long et al., Reliability-aware service provisioning in NFV-enabled enterprise datacenter networks, Int. Conf. Netw. Service Manag., Montreal, Canada, Oct. 31-Nov. 4, 2016, pp. 153-159.
  12. Q. Long, M. Khabbaz, and C. Assi, Reliability‐aware service chaining in carrier‐grade softwarized networks, IEEE J. Sel. Areas Commun. 36 (2018), no. 3, 558-579. https://doi.org/10.1109/JSAC.2018.2815338
  13. J. Fan et al., GREP: guaranteeing reliability with enhanced protection in NFV, ACM SIGCOMM Workshop Hot Topics Middleboxes Netw. Function Virtualization, London, UK, Aug. 21, 2015, pp. 13-18.
  14. J. Fan et al., Availability-aware mapping of service function chains, IEEE INFOCOM 2017 - IEEE Conf. Comput. Commun., Atlanta, GA, May 1-4, 2017, pp. 1-9.
  15. F. Carpio and A. Jukan, Improving reliability of service function chains with combined VNF migrations and replications, arXiv:1711.08965, 2017.
  16. P. Gill, N. Jain, and N. Nagappan, Understanding network failures in data centers: measurement, analysis, and implications, ACM SIGCOMM Comput. Commun. Rev. 41 (2011), no. 4, 350-361. https://doi.org/10.1145/2043164.2018477
  17. J. Liu et al., Reliability evaluation for NFV deployment of future mobile broadband networks, IEEE Wireless Commun. 23 (2016), no. 3, 90-96. https://doi.org/10.1109/MWC.2016.7498079
  18. R. Potharaju and N. Jain, Demystifying the dark side of the middle: a field study of middlebox failures in datacenters, Conf. Int. Measurement Conf., Barcelona, Spain, Oct. 23-25, 2013, pp. 9-22.
  19. A. Hmaity et al., Protection strategies for virtual network functions placement and service chains provisioning, Netw. 70 (2017), no. 4, 373-387. https://doi.org/10.1002/net.21782
  20. M. Scholler et al., Resilient deployment of virtual network functions, Int. Congress Ultra Modern Telecommun. Contr. Syst. Workshops, Almaty, Kazahstan, Sept. 10-13, 2013, pp. 208-214.
  21. J. Kwisthout, Most probable explanations in Bayesian networks: complexity and tractability, Int. J. Approx. Reason. 52 (2011), no. 9, pp. 1452-1469. https://doi.org/10.1016/j.ijar.2011.08.003
  22. Z. Han et al., Dynamic virtual machine management via approximate Markov decision process, IEEE INFOCOM 2016 - IEEE Int. Conf. Comput. Commun., San Francisco, CA, Apr. 10-14, 2016, pp. 1-9.
  23. S. Wang et al., Deep reinforcement learning for dynamic multichannel access in wireless networks, IEEE Trans. Cognitive Commun. Netw. 4 (2018), no. 2, 257-265. https://doi.org/10.1109/TCCN.2018.2809722
  24. B. Zoph and Q. V. Le, Neural architecture search with reinforcement learning, arXiv:1611.01578, 2016.
  25. M. L. Puterman, Markov decision processes: discrete stochastic dynamic programming, John Wiley & Sons, Inc, New York, NY, 2005.
  26. F. G. Harmon, A. A. Frank, and S. S. Joshi, The control of a parallel hybrid‐electric propulsion system for a small unmanned aerial vehicle using a CMAC neural network, Neural Netw. 18 (2005), no. 5, 772-780. https://doi.org/10.1016/j.neunet.2005.06.030
  27. M. Yu, Y. Yi, and J. Rexford, Rethinking virtual network embedding: substrate support for path splitting and migration, ACM SIGCOMM Comput. Commun. Rev. 38 (2008), no. 2, 17-29. https://doi.org/10.1145/1355734.1355737
  28. E. W. Zegura, K. L. Calvert, and S. Bhattacharjee, How to model an internetwork, Proc. IEEE Infocom, Conf. Comput. Commun., San Francisco, CA, Mar. 24-28, 1996, pp. 589-594.
  29. S. Orlowski et al., SNDlib 1.0-survivable network design library, Netw. 55 (2010), no. 3, pp. 276-286. https://doi.org/10.1002/net.20371
  30. Google, Google apps service level agreement, Nov. 3, 2016. Available at http://www.google.com/apps/intl/en/terms/sla/html
  31. M. F. Bari et al., On orchestrating virtual network functions, Int. Conf. Netw. Service Manag., Barcelona, Spain, Nov. 9-13, 2015, pp. 50-56.