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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2020

06.02.2020

A Dominance of the Channel Capacity in Load Balancing of Software Defined Network

verfasst von: Vivek Srivastava, Ravi Shankar Pandey

Erschienen in: Wireless Personal Communications | Ausgabe 3/2020

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Abstract

Software defined networks concept is used to transfer the responsibility of route management from forwarding devices to a centralized control application which is called a controller. The controller is used to decide the congestion-free path between source and destination. In data transfer, channel capacity plays an important role. In many situations channel capacity is below some threshold value which causes congestion and decreases the reliability of the network. Several requests may contact the controller for getting the optimal path. This increases the load at the controller side. The controller decides the path to improve the reliability of the network as well as to maximize the uses of channel capacity in the network. In this paper, we have proposed an algorithm for load balancing. We have considered our network as a graph, the vertices of the graph are switches and the channels of the network are represented as edges and considered the dominance of the channel capacity over server load for deciding the optimal path. We have demonstrated our algorithm using a mathematical example.
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Literatur
1.
Koushika, A. M., & Selvi, S. T. (2014). Load valancing using software defined networking in cloud environment. In 2014 international conference on recent trends in information technology (pp. 1–8). IEEE.
2.
Ld, D. B., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing,13(5), 2292–2303.CrossRef
3.
Bebali, A., El Asri, B., & Kriouile, H. (2015). A pareto based artificial bees colony and product line for optimizing scheduling of VM on cloud computing. IEEE.
4.
Di Stefano, A., Cammarata, G., Morana, G., & Zito, D. (2015). A4SDN-adaptive alienated ant algorithm for software-defined networking. In 2015 10th international conference on P2P, parallel, grid, cloud and internet computing (3PGCIC) (pp. 344–350). IEEE.
5.
Kashani, M. H., Jamei, M., Akbari, M., & Tayebi, R. M. (2011). Utilizing bee colony to solve task scheduling problem in distributed systems. In 2011 third international conference on computational intelligence, communication systems and networks (pp. 298–303). IEEE.
6.
Snyder, P. L., Valetto, G., Fernandez-Marquez, J. L., & Serugendo, G. D. M. (2012). Augmenting the repertoire of design patterns for self-organized software by reverse engineering a bio-inspired p2p system. In 2012 IEEE sixth international conference on self-adaptive and self-organizing systems (pp. 199–204). IEEE.
7.
Nimbark, H., Sukhadia, R., & Kotak, P. P. (2014). Optimizing architectural properties of artificial neural network using proposed artificial bee colony algorithm. In 2014 international conference on advances in computing, communications and informatics (ICACCI) (pp. 1285–1289). IEEE.
8.
Su, W., Liu, C., Lagoa, C. M., Che, H., Xu, K., & Cui, Y. (2015). Integrated, distributed traffic control in multidomain networks. IEEE Transactions on Control Systems Technology,23(4), 1373–1386.CrossRef
9.
Kamiyama, N., Takahashi, Y., Ishibashi, K., Shiomoto, K., Otoshi, T., Ohsita, Y., & Murata, M. (2014). Flow aggregation for traffic engineering. In 2014 IEEE global communications conference (pp. 1936–1941). IEEE.
10.
Craig, A., Nandy, B., Lambadaris, I., & Ashwood-Smith, P. (2015). Load balancing for multicast traffic in SDN using real-time link cost modification. In 2015 IEEE international conference on communications (ICC) (pp. 5789–5795). IEEE.
11.
Amiri, M., Al Osman, H., Shirmohammadi, S., & Abdallah, M. (2015). An SDN controller for delay and jitter reduction in cloud gaming. In Proceedings of the 23rd ACM international conference on multimedia (pp. 1043–1046). ACM.
12.
Hu, Y., Wang, W., Gong, X., Que, X., & Cheng, S. (2012). Balanceflow: Controller load balancing for openflow networks. In 2012 IEEE 2nd international conference on cloud computing and intelligence systems (Vol. 2, pp. 780–785). IEEE.
13.
Zhang, Y. (2013). An adaptive flow counting method for anomaly detection in SDN. In Proceedings of the ninth ACM conference on emerging networking experiments and technologies (pp. 25–30). ACM.
14.
Li, J., Chang, X., Ren, Y., Zhang, Z., & Wang, G. (2014). An effective path load balancing mechanism based on SDN. In 2014 IEEE 13th international conference on trust, security and privacy in computing and communications (pp. 527–533). IEEE.
15.
Adami, D., Giordano, S., Pagano, M., & Santinelli, N. (2014). Class-based traffic recovery with load balancing in software-defined networks. In 2014 IEEE globecom workshops (GC Wkshps) (pp. 161–165). IEEE.
16.
Carlinet, Y., & Perrot, N. (2016). Energy-efficient load balancing in a SDN-based data-center network. In 2016 17th international telecommunications network strategy and planning symposium (Networks) (pp. 138–143). IEEE.
17.
Raeisi, B., & Giorgetti, A. (2016). Software-based fast failure recovery in load balanced SDN-based datacenter networks. In 2016 6th international conference on information communication and management (ICICM) (pp. 95–99). IEEE.
18.
Adami, D., Giordano, S., Pagano, M., & Portaluri, G. (2016). A novel SDN controller for traffic recovery and load balancing in data centers. In 2016 IEEE 21st international workshop on computer aided modelling and design of communication links and networks (CAMAD) (pp. 77–82). IEEE.
19.
Adalian, N., Ajaeiya, G., Dawy, Z., Elhajj, I. H., Kayssi, A., & Chehab, A. (2016). Load balancing in LTE core networks using SDN. In 2016 IEEE international multidisciplinary conference on engineering technology (IMCET) (pp. 213–217). IEEE.
20.
Uppal, H., & Brandon, D. (2010). OpenFlow based load balancing. CSE561: Networking project report, University of Washington.
21.
Li, Y., & Pan, D. (2013). OpenFlow based load balancing for fat-tree networks with multipath support. In Proceedings of the 12th IEEE international conference on communications (ICC’13), Budapest, Hungary (pp. 1–5).
22.
Chen, W., Shang, Z., Tian, X., & Li, H. (2015). Dynamic server cluster load balancing in virtualization environment with openflow. International Journal of Distributed Sensor Networks,11(7), 531538.CrossRef
23.
Sahoo, K. S., Tiwary, M., & Sahoo, B. (2017). A load prediction model for SDN controllers. International Journal of automatic computing, 2(4), 1–16.
24.
Aljammal, A. H., Manasrah, A. M., Abdallah, A. E., & Tahat, N. M. (2017). A new architecture of cloud computing to enhance the load balancing. International Journal of Business Information Systems,25(3), 393–405.CrossRef
25.
Kitagami, S., Kaneko, Y., Kiyohara, R., & Suganuma, T. (2013). Autonomic load balancing for M2M communication with long-polling. International Journal of Space-Based and Situated Computing,3(1), 45–54.CrossRef
26.
Jammal, M., Singh, T., Shami, A., Asal, R., & Li, Y. (2014). Software defined networking: State of the art and research challenges. Computer Networks,72, 74–98.CrossRef
27.
Hsin, H. K., Chang, E. J., Chao, C. H., & Wu, A. Y. (2010). Regional ACO-based routing for load-balancing in NoC systems. In 2010 second world congress on nature and biologically inspired computing (NaBIC) (pp. 370–376). IEEE.
28.
Bertsekas, D., Gafni, E., & Gallager, R. (1984). Second derivative algorithms for minimum delay distributed routing in networks. IEEE Transactions on Communications,32(8), 911–919.MathSciNetCrossRef
Metadaten
Titel
A Dominance of the Channel Capacity in Load Balancing of Software Defined Network
verfasst von
Vivek Srivastava
Ravi Shankar Pandey
Publikationsdatum
06.02.2020
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-020-07130-7

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