EXPRL:Experience And Prediction Based Load Balancing Strategy For Multi-Controller Software Defined Networks
| Autor: | Banerjee, Anuradha, Hussain, Dil Muhammad Akbar |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Banerjee, A & Hussain, D M A 2019, EXPRL : Experience And Prediction Based Load Balancing Strategy For Multi-Controller Software Defined Networks . in Proceedings of 2nd International Multi-Topic Conference on Engineering and Science (IMCES 19) ., 126, Gyancity International Publishers, 2nd International Multi-Topic Conference on Engineering and Science (IMCES 2019), Mauritius, Hawaii, United States, 05/05/2019 . |
| Popis: | Software Defined Networks or SDN has proved itself to be backbone in network design of the new era and is quickly becoming an industry standard. It allows decoupling of control and data plane for efficient monitoring of the network traffic. These networks can be divided into two main classes – single and multiple controller SDN. A single controller is unable to control flows if number of switches connected to it increase up to a great extent and traffic generated by them overload the controller. In that case a pool of multiple controllers is a necessity. There each controller is initially connected to approximately same number of switches. Still if a controller feels overloaded then some of its switches are disconnected from it and connected to some comparatively under loaded controller to balance load in the network. The present article EXPRL proposes an experience and prediction based load balancing strategy that efficiently identifies overloaded controllers and selects target underloadedcontrollers to shift some load from the overloaded ones. Simulation results show that EXPRL enables the network to greatly increase network throughput and reduce network latency as well as migration cost than its state-of-the-art competitors. |
| Databáze: | OpenAIRE |
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