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Derya, Yiltas-Kaplan
- Web-Based User Interface for the Floodlight SDN Controller
Abstract Views :229 |
PDF Views:5
Authors
Affiliations
1 Department of Computer Engineering, Istanbul University, Istanbul, TR
1 Department of Computer Engineering, Istanbul University, Istanbul, TR
Source
International Journal of Advanced Networking and Applications, Vol 8, No 5 (2017), Pagination: 3175-3180Abstract
Software Defined Networking (SDN) was born as a solution for next-generation network design. Due to its flexible architecture, SDN promises to make network devices simpler while giving better centralized control ability over network and improving parameters such as flexibility, resilience, reliability, and security. In this paper, we briefly introduce the SDN architecture and the Floodlight Controller that is one of the popular SDN controllers. We build a web-based user interface for the Floodlight Controller by using REST API. This application is the first program in the Floodlight SDN Controller literature to view the controller upon several properties such as device connections and flow tables.Keywords
Floodlight Controller, OpenFlow, Programmable Networks, SDN Web, Software-Defined Network.References
- A. Shahid, J. Fiaidhi and S. Mohammed, Implementing Innovative Routing Using Software Defined Networking (SDN), International Journal of Multimedia and Ubiquitous Engineering, 11(2), 2016, 159-172.
- G. Wiley, Software networks, virtualization, sdn, 5g and security (Great Britain: ISTE Ltd and John Wiley & Sons, 2015).
- K. Ahokas, Software-defined networking, Aalto University CSE-E4430 Methods and Tools for Network Systems, Finland, Autumn 2014.
- Pooja, M. Sood, SDN and Mininet: Some Basic Concepts, Int. J. Advanced Networking and Applications, 07(02), 2015, 2690-2693.
- B. A. A. Nunes, M. Mendonca, X. N. Nguyen, K. Obraczka and T. Turletti, A Survey of SoftwareDefined Networking: Past, Present, and Future of Programmable Networks, IEEE Communications Surveys & Tutorials, 16(3), 1617-1634.
- N. Feamster, J. Rexford, and E. Zegura, The road to SDN: an intellectual history of programmable networks, SIGCOMM Comput. Commun, 44(2), 2014, 87-98.
- D. Kreutz, F. M. V. Ramos, P. E. Veríssimo, C. E. Rothenberg, S. Azodolmolky and S. Uhlig, Software-Defined Networking: A Comprehensive Survey, in Proceedings of the IEEE, 103(1), 2015, 14-76.
- P. Göransson, C. Black, Software defined networks (USA: Morgan Kaufmann, 2014).
- Y. Jarraya, T. Madi and M. Debbabi, A Survey and a Layered Taxonomy of Software-Defined Networking, IEEE Communications Surveys & Tutorials, 16(4), 2014, 1955-1980.
- T. D. Nadeau, K. Gray, SDN: software defined networks (USA: O’Reilly, 2013).
- R. Izard (administrator), H. Akcay (GUI developer), (2017). Floodlight WEB GUI. [online] Floodlight.atlassian.net. Available at: https://floodlight.atlassian.net/wiki/spaces/floodlight controller/pages/40403023/Web+GUI [Accessed 17 Mar. 2017].
- Multiple-Criteria Decision Analysis:A Novel Rank Aggregation Method
Abstract Views :171 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Engineering, Istanbul University, 34320, Avcilar, Istanbul, TR
1 Department of Computer Engineering, Istanbul University, 34320, Avcilar, Istanbul, TR
Source
International Journal of Advanced Networking and Applications, Vol 9, No 5 (2018), Pagination: 3537-3544Abstract
Ranking among several objects is a very crucial operation for different applications to find a vote value for each object against the others. Multiple metrics can be combined to get a single vote value of an object. There are many studies in the literature that convert the ranking problem into a graph structure to solve it with a discrete mathematical process. Generally, these studies define multiple metrics as matrix forms and then relate them with the computations of eigenvectors to find the best ranked object. However, due to the dynamic nature of the metric values, ranking approaches should be fast and less complex. In this study a different approach for the ranking process with multiple metrics is proposed. This approach is fast and easy to implement. In order to test the approach, a network scenario is designed with computer programs. The experimental results show that this method outperforms a common conventional method in terms of various metric values, namely transmission time, packet loss rate, jitter, availability, and throughput. As a consequence, the proposed method gives the average value of each individual metric as more advantageous and without rescaling the numerical values.Keywords
Decision Theory, Multi-Criteria Decision Analysis, Multi-objective Decision, Rank Aggregation, Rank Centrality.References
- V. Conitzer, Making decisions based on the preferences of multiple agents, Communications of the ACM, 53, 2010, 84-94.
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- C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, Rank aggregation methods for the web, Proc. Tenth Int. World Wide Web Conference (WWW10), Hong Kong, 2001, 613-622.
- Z. Gormez, E. Gumus, A. Sertbas, and O. Kursun, Comparison of aggregators for multi-objective SNP selection, Proc. 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, 2013, 3062-3065.
- F.E.M. Arasi, A. Anand, and S. Kumar, QoS based ranking for composite web services, International Journal of Science, Engineering and Technology Research (IJSETR), 3, 2014, 1041-1046.
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- A. Yazdani, L. Dueñas-Osorio, and Q. Li, A scoring mechanism for the rank aggregation of network robustness, Commun Nonlinear Sci Numer Simulat, 18, 2013, 2722–2732.
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- R. Kumar, Rank aggregation. Lecture Notes, University of Rome, Italy, 2008.
- S. Negahban, S. Oh, and D. Shah, Rank centrality: Ranking from pair-wise comparisons, http://arxiv.org/abs/1209.1688v2, Cornell University Library. Presented in part at NIPS, 2012 in Lake Tahoe. Last accessed: March 2018.