Volume 9, Issue 6 (June 2022), Pages: 134-144

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 Original Research Paper

 The performance effect due to varying network topologies on a software-defined network employing the k-shortest path

 Author(s): R. Linsheng ¹, M. N. Derahman ¹, M. F. A. Kadir ^2, *, M. A. Mohamed ², S. Kamarudin ¹

 Affiliation(s):

 ¹Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan, Malaysia
 ²Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-3647-5134

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.06.018

 Abstract:

Packet routing has always been an issue that affects network performance. The traditional protocol is known to work with local information and thus not able to produce a global optimal decision. In the software-defined network (SDN), its centralized controller obtains unified access to the entire network topology information and has the ability to partially solve traditional network packet forwarding problems. SDN controller uses the Dijkstra algorithm to find the shortest path calculated from the source node to the destination node. However, constraints such as the need to bypass the node which has a high rate of failure exist to prevent the Dijkstra algorithm from meeting this demand. In practice, we need not only consider the shortest path but also consider the second short path, the third short path, and so on. K-shortest paths algorithm discovers a set of paths ordered in the most optimal, optimal, and suboptimal, has a very wide application is integrated into SDN controller to handle routing functionality. In this study, instead of only the number of hops, bandwidth and delay are adhered to within the k-shortest path to select the final route. Under this circumstance, different topologies are examined in response to network bandwidth and packet delay. The experiment shows that tree topology is best suited for improving bandwidth while simple topology for reducing delay.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Software-defined network, Traffic forwarding, K-Shortest path, Dijkstra algorithm

 Article History: Received 2 December 2021, Received in revised form 10 March 2022, Accepted 11 April 2022

 Acknowledgment 

This project is partially funded by the Center for Research Excellence, Incubation Management Center, Universiti Sultan Zainal Abidin, Malaysia.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Linsheng R, Derahman MN, and Kadir MFA et al. (2022). The performance effect due to varying network topologies on a software-defined network employing the k-shortest path. International Journal of Advanced and Applied Sciences, 9(6): 134-144

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 Figures

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 References (20)

1. Akin E and Korkmaz T (2019). Comparison of routing algorithms with static and dynamic link cost in software defined networking (SDN). IEEE Access, 7: 148629-148644. https://doi.org/10.1109/ACCESS.2019.2946707   [Google Scholar]
2. Akyildiz IF, Lee A, Wang P, Luo M, and Chou W (2014). A roadmap for traffic engineering in SDN-OpenFlow networks. Computer Networks, 71: 1-30. https://doi.org/10.1016/j.comnet.2014.06.002   [Google Scholar]
3. Benzekki K, El Fergougui A, and Elbelrhiti Elalaoui A (2016). Software‐defined networking (SDN): A survey. Security and Communication Networks, 9(18): 5803-5833. https://doi.org/10.1002/sec.1737   [Google Scholar]
4. Bi Y, Han G, Lin C, Peng Y, Pu H, and Jia Y (2019). Intelligent quality of service aware traffic forwarding for software-defined networking/open shortest path first hybrid industrial internet. IEEE Transactions on Industrial Informatics, 16(2): 1395-1405. https://doi.org/10.1109/TII.2019.2946045   [Google Scholar]
5. Brander AW and Sinclair MC (1996). A comparative study of k-shortest path algorithms. In: Merabti M, Carew M, and Ball F (Eds.), Performance engineering of computer and telecommunications systems: 370-379. Springer, London, UK. https://doi.org/10.1007/978-1-4471-1007-1_25   [Google Scholar]
6. Chen BY, Chen XW, Chen HP, and Lam WH (2020). Efficient algorithm for finding k shortest paths based on re-optimization technique. Transportation Research Part E: Logistics and Transportation Review, 133: 101819. https://doi.org/10.1016/j.tre.2019.11.013   [Google Scholar]
7. Cheng C, Riley R, Kumar SP, and Garcia-Luna-Aceves JJ (1989). A loop-free extended Bellman-Ford routing protocol without bouncing effect. ACM SIGCOMM Computer Communication Review, 19(4): 224-236. https://doi.org/10.1145/75247.75269   [Google Scholar]
8. Dijkstra EW (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1): 269-271. https://doi.org/10.1007/BF01386390   [Google Scholar]
9. Elias SJ, Hatim SM, Darus MY, Abdullah S, Jasmis J, Ahmad RB, and Khang AWY (2019). Congestion control in vehicular Adhoc network: A survey. Indonesian Journal of Electrical Engineering and Computer Science, 13(3): 1280-1285. https://doi.org/10.11591/ijeecs.v13.i3.pp1280-1285   [Google Scholar]
10. Karagiannis G, Altintas O, Ekici E, Heijenk G, Jarupan B, Lin K, and Weil T (2011). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Communications Surveys and Tutorials, 13(4): 584-616. https://doi.org/10.1109/SURV.2011.061411.00019   [Google Scholar]
11. Latré B, Braem B, Moerman I, Blondia C, and Demeester P (2011). A survey on wireless body area networks. Wireless Networks, 17(1): 1-18. https://doi.org/10.1007/s11276-010-0252-4   [Google Scholar]
12. Luong DH, Outtagarts A, and Hebbar A (2016). Traffic monitoring in software defined networks using open daylight controller. In the International Conference on Mobile, Secure, and Programmable Networking, Springer, Paris, France: 38-48. https://doi.org/10.1007/978-3-319-50463-6_4   [Google Scholar]
13. Muzakkari BA, Mohamed MA, Kadir MF, Mohamad Z, and Jamil N (2018). Recent advances in energy efficient-QoS aware MAC protocols for wireless sensor networks. International Journal of Advanced Computer Research, 8(38): 212-228. https://doi.org/10.19101/IJACR.2018.837016   [Google Scholar]
14. Nisar K, Jimson ER, Hijazi MHA, Welch I, Hassan R, Aman AHM, and Khan S (2020). A survey on the architecture, application, and security of software defined networking: Challenges and open issues. Internet of Things, 12: 100289. https://doi.org/10.1016/j.iot.2020.100289   [Google Scholar]
15. Noto M and Sato H (2000). A method for the shortest path search by extended Dijkstra algorithm. In the SMC 2000 Conference Proceedings: 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to Systems, Humans, Organizations, and Their Complex Interactions'(cat. no. 0), IEEE, Ashville, USA, 3: 2316-2320. https://doi.org/10.1109/ICSMC.2000.886462   [Google Scholar]
16. Rout S, Sahoo KS, Patra SS, Sahoo B, and Puthal D (2021). Energy efficiency in software defined networking: A survey. SN Computer Science, 2(4): 1-15. https://doi.org/10.1007/s42979-021-00659-9   [Google Scholar]
17. Shallahuddin AA, Kadir MFA, Mohamed MA, Usop NSM, and Zakaria ZA (2020). An enhanced adaptive duty cycle scheme for optimum data transmission in wireless sensor network. In: Kim K and Kim HY (Eds.), Information science and applications: 33-40. Springer, Singapore, Singapore. https://doi.org/10.1007/978-981-15-1465-4_4   [Google Scholar]
18. Sinha Y, Vashishth S, and Haribabu K (2017). Real time monitoring of packet loss in software defined networks. In the International Conference on Ubiquitous Communications and Network Computing, Springer, Bangalore, India: 154-164. https://doi.org/10.1007/978-3-319-73423-1_14   [Google Scholar]
19. Tsitsiashvili GS and Losev AS (2008). Application of the Floyd algorithm to the asymptotic analysis of networks with unreliable ribs. Automation and Remote Control, 69(7): 1262-1265. https://doi.org/10.1134/S0005117908070175   [Google Scholar]
20. Xia W, Wen Y, Foh CH, Niyato D, and Xie H (2014). A survey on software-defined networking. IEEE Communications Surveys and Tutorials, 17(1): 27-51. https://doi.org/10.1109/COMST.2014.2330903   [Google Scholar]