Scalability and Reliability Aware SDN Controller Placement Strategies

F. Bannour S. Souihi 1 A. Mellouk 1
1 CIR
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : The decoupling of control and data planes in Software-Defined Networking (SDN) brings benefits in terms of logically centralized control and application programming. But, the single point of management in physically centralized SDN architectures is a potential point of failure and a bottleneck that compromises network reliability and performance. Such centralized designs may also face scalability challenges especially in networks with a large number of hosts (e.g. IoT-like networks). To avoid such concerns, SDN control architectures are usually designed as physically distributed systems. This raises practical challenges about the best approach to decentralizing the control plane while maintaining the logically centralized network view. In particular, determining the number of controllers and locating them in the network is a hard task that should be addressed appropriately. This paper proposes two novel strategies that cover different aspects of the controller placement problem with respect to performance and reliability criteria. These strategies use two types of heuristics that are compared and assessed on large-scale topologies to provide operators with guidelines on how to find their optimal controller placement that meets their specific needs.
Type de document :
Communication dans un congrès
Proc. Of the 13th International Conf. on Network and Service Management, CNSM 2017, MANSDNNFV2017, 2017, Tokyo, Japan. 2017
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https://hal.archives-ouvertes.fr/hal-01676567
Contributeur : Lab Lissi <>
Soumis le : vendredi 5 janvier 2018 - 17:44:12
Dernière modification le : mercredi 20 février 2019 - 10:36:34

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  • HAL Id : hal-01676567, version 1

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F. Bannour, S. Souihi, A. Mellouk. Scalability and Reliability Aware SDN Controller Placement Strategies. Proc. Of the 13th International Conf. on Network and Service Management, CNSM 2017, MANSDNNFV2017, 2017, Tokyo, Japan. 2017. 〈hal-01676567〉

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