Disaster-Aware Dynamic Routing for SDN-Based Multi-Site Data Center Networks

Xiaochen Li

1. School of Systems Information Science, Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate, Hokkaido, 041-8655, Japan
2. PLA Strategic Support Force Information Engineering University, Zhengzhou, Henan, 450001, PR China
3. School of Computer and Information Engineering, Chuzhou University, Anhui 239000, PR China

In recent years, cloud computing technology has been developing rapidly. As a result, the internal traffic of large-scale enterprises’ data centers has increased significantly. It has become important to improve the disaster tolerance capability of data centers to ensure user data security. However, the data center network relies on its physical infrastructure. Large-scale disasters may damage the infrastructure and cause huge data loss or connection interruption. Software-defined network (SDN) is an innovative network architecture that separates the control and forwarding layers of the network. Thus, SDN promotes network programmability and opens up new ways to design disaster-resistant networks. Based on SDN technology, we propose a disaster-aware dynamic routing (DADR) scheme. When a disaster signal is received, the SDN controller notifies the Global Server Load Balance (GSLB) device and stops declaring the IP of the disaster-stricken data center. At the same time, the SDN controller sends the server and client session information and the current traffic information of the disaster-stricken data center to other sites, where the optimal routing path is calculated for the data center based on the traffic characteristics by the proposed Lagrangian Relaxation based Bellman-Ford Parallel algorithm (LRBFP). Our results show that in the event of a disaster, based on the proposed DADR scheme and LRBFP algorithm, the packet loss rate and network delay can be greatly reduced.

Disaster-aware, dynamic routing, data center networks, software-defined network, parallel algorithm

Wenhao Zhang, Xiaochen Li, and Lisheng Ma (2021). Disaster-Aware Dynamic Routing for SDN-Based Multi-Site Data Center Networks. Journal of Networking and Network Applications, Volume 1, Issue 1, pp. 9–18. https://doi.org/10.33969/J-NaNA.2021.010102.

[1] T. Adachi, Y. Ishiyama, Y. Asakura, and K. Nakamura, “The restoration of telecom power damages by the great east japan earthquake,” in 2011 IEEE 33rd International Telecommunications Energy Conference (INTELEC). IEEE, 2011, pp. 1–5.
[2] “Flooding, power outages from hurricane sandy lead to internet, phone service disruptions.” https://nypost.com/2012/10/30/flooding-power-outages-from-hurricane-sandy-lead-to-internet-phone-service-disruptions, [Accessed: Dec 2020].
[3] F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Bur-rows, T. Chandra, A. Fikes, and R. E. Gruber, “Bigtable: A distributed storage system for structured data,” ACM Transactions on Computer Systems, vol. 26, no. 2, pp. 1–26, 2008.
[4] G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman,
A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels, “Dynamo: amazon’s highly available key-value store,” ACM SIGOPS operating systems review, vol. 41, no. 6, pp. 205–220, 2007.
[5] S. Ghemawat, H. Gobioff, and S.-T. Leung, “The google file system,” in Proceedings of the nineteenth ACM symposium on Operating systems principles, 2003, pp. 29–43.
[6] R. S. Narayana, M. Raja, R. Mutnuru, and R. Kondamuru, “Systems and methods for gslb mep connection management across multiple core appliances,” Apr. 2 2013, uS Patent 8,412,832.
[7] J.-B. Moon and M.-H. Kim, “Dynamic load balancing method based on dns for distributed web systems,” in International Conference on Electronic Commerce and Web Technologies. Springer, 2005, pp. 238–247.
[8] N. McKeown, “Software-defined networking,” INFOCOM keynote talk, vol. 17, no. 2, pp. 30–32, 2009.
[9] N. Handigol, S. Seetharaman, M. Flajslik, N. McKeown, and R. Jo-hari, “Plug-n-serve: Load-balancing web traffic using openflow,” ACM Sigcomm Demo, vol. 4, no. 5, 2009.
[10] M. Koerner and O. Kao, “Multiple service load-balancing with open-flow,” in 13th International Conference on High Performance Switching and Routing. IEEE, 2012, pp. 210–214.
[11] D. Chen, Z. Liu, L. Wang, M. Dou, J. Chen, and H. Li, “Natural disaster monitoring with wireless sensor networks: a case study of data-intensive applications upon low-cost scalable systems,” Mobile Networks and Applications, vol. 18, no. 5, pp. 651–663, 2013.
[12] S. Kandula, S. Sengupta, A. Greenberg, P. Patel, and R. Chaiken, “The nature of data center traffic: measurements & analysis,” in Proceedings of the 9th ACM SIGCOMM conference on Internet measurement. ACM, 2009, pp. 202–208.
[13] T. Benson, A. Akella, and D. A. Maltz, “Network traffic characteristics of data centers in the wild,” in Proceedings of the 10th ACM SIGCOMM conference on Internet measurement. ACM, 2010, pp. 267–280.
[14] D. Li, M. Xu, H. Zhao, and X. Fu, “Building mega data center from heterogeneous containers,” in 19th IEEE International Conference on Network Protocols. IEEE, 2011, pp. 256–265.
[15] A. R. Curtis, W. Kim, and P. Yalagandula, “Mahout: Low-overhead datacenter traffic management using end-host-based elephant detection,” in INFOCOM. IEEE, 2011, pp. 1629–1637.
[16] S. Kandula, D. Katabi, B. Davie, and A. Charny, “Walking the tightrope: Responsive yet stable traffic engineering,” ACM SIGCOMM Computer Communication Review, vol. 35, no. 4, pp. 253–264, 2005.
[17] H. Wang, H. Xie, L. Qiu, Y. R. Yang, Y. Zhang, and A. Greenberg, “Cope: traffic engineering in dynamic networks,” in Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications, 2006, pp. 99–110.
[18] B. Fortz and M. Thorup, “Internet traffic engineering by optimizing ospf weights,” in INFOCOM, vol. 2. IEEE, 2000, pp. 519–528.
[19] D. Xu, M. Chiang, and J. Rexford, “Link-state routing with hop-by-hop forwarding can achieve optimal traffic engineering,” IEEE/ACM Transactions on networking, vol. 19, no. 6, pp. 1717–1730, 2011.
[20] R. Ahujia, T. L. Magnanti, and J. B. Orlin, “Network flows: Theory, algorithms and applications,” New Jersey: Rentice-Hall, 1993.
[21] A. S. Nepomniaschaya and M. A. Dvoskina, “A simple implementation of dijkstra’s shortest path algorithm on associative parallel processors,” Fundamenta Informaticae, vol. 43, no. 1-4, pp. 227–243, 2000.
[22] P. Harish and P. Narayanan, “Accelerating large graph algorithms on the gpu using cuda,” in International conference on high-performance computing. Springer, 2007, pp. 197–208.
[23] “Opendaylight project proposal [eb/ol],” http://www.opendaylight.org/,[Accessed: May 2020].
[24] “Nvidia. programming guide: Cuda toolkit documentation.” https://docs.nvidia.com/cuda/, [Accessed: May 2020].
[25] A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel, “The cost of a cloud: research problems in data center networks,” 2008.
[26] C. Hopps, “Analysis of an equal-cost multi-path algorithm,” RFC 2992, Internet Engineering Task Force, 2000.
[27] J.-R. Jiang, H.-W. Huang, J.-H. Liao, and S.-Y. Chen, “Extending dijkstra’s shortest path algorithm for software defined networking,” in The 16th Asia-Pacific Network Operations and Management Symposium. IEEE, 2014, pp. 1–4.
[28] E. W. Dijkstra et al., “A note on two problems in connexion with graphs,” Numerische mathematik, vol. 1, no. 1, pp. 269–271, 1959.
[29] P. Erd˝os and A. R´enyi, “On the evolution of random graphs,” Publ. Math. Inst. Hung. Acad. Sci, vol. 5, no. 1, pp. 17–60, 1960.