The controller placement problem for wireless SDN
Wireless Networks, 2019•Springer
The software defined networking paradigm decouples the network's control logic (the control
plane) from the underlying routers and switches (the data plane), promoting centralization of
network control. The controller placement problem is threefold in nature: the number of
controllers to be placed in a network, the locations of these controllers and the assignment
function of controllers to switches, with all of them important for the design of an efficient
control plane. Most of the existing literature focuses on the placement problem assuming the …
plane) from the underlying routers and switches (the data plane), promoting centralization of
network control. The controller placement problem is threefold in nature: the number of
controllers to be placed in a network, the locations of these controllers and the assignment
function of controllers to switches, with all of them important for the design of an efficient
control plane. Most of the existing literature focuses on the placement problem assuming the …
Abstract
The software defined networking paradigm decouples the network’s control logic (the control plane) from the underlying routers and switches (the data plane), promoting centralization of network control. The controller placement problem is threefold in nature: the number of controllers to be placed in a network, the locations of these controllers and the assignment function of controllers to switches, with all of them important for the design of an efficient control plane. Most of the existing literature focuses on the placement problem assuming the medium between the controllers and the switches is wired. In this paper, we present a novel strategy to address the controller placement problem, which protects the latency, link failure probability and transparency in the case of a wireless SouthBound interface. We model the problem of determining the placement of wireless controllers in software defined networking. For this purpose, we present a heuristic solution, based on the simulated annealing genetic algorithm, which provides a fast and efficient solution.
Springer
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