Robust hierarchical control plane for Transport Software-Defined Networks

Lourenco, Rafael B. R.; Savas, S. Sedef; Tornatore, Massimo; Mukherjee, Biswanath

doi:10.1016/j.osn.2018.05.004

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…To deal with above two factors, T-SDN focus on a hierarchical control plane [32]. The lower level of this hierarchical structure is composed of controllers, each capable of managing elements in a certain network area.…”

Section: Problem Definitionmentioning

confidence: 99%

Reliable Control and Data Planes for Softwarized Networks

Mas-Machuca

Musumeci

Vizarreta

et al. 2020

Computer Communications and Networks

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Section: Problem Definitionmentioning

confidence: 99%

Reliable Control and Data Planes for Softwarized Networks

Mas-Machuca

Musumeci

Vizarreta

et al. 2020

Computer Communications and Networks

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“…On the other hand, control-plane architectures [12] [16] in T-SDN paradigm are designed to accommodate multiple heterogeneous network domains and associated domain-specific ON controllers. But, as recent studies [2] [16] suggests, there is still no consensus on the design of the T-SDN control plane. Ref.…”

Section: Background and Related Workmentioning

confidence: 99%

“…But, if the controller capacity constraint fails (line 9), the algorithm checks if the current hosting location (h) has enough compute and memory capacity to host the additional δ controllers (line 13). If yes, we turn on additional controllers and load balance the switches and traffic flows (line [14][15][16][17]. If host location h does not have enough resources, the algorithm finds the next optimal location to host all the instances (line 20) following constraints as in Eqns.…”

Section: E Algorithmmentioning

confidence: 99%

Virtualized Controller Placement for Multi-domain Optical Transport Networks

Rahman¹,

Ahmed²,

Ferdousi³

et al. 2020

Optical Network Design and Modeling

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Optical multi-domain transport networks are often controlled by a hierarchical distributed architecture of controllers. Optimal placement of these controllers is very important for their efficient management and control. Traditional SDN controller placement methods focus mostly on controller placement in datacenter networks. But the problem of virtualized controller placement for multi-domain transport networks needs to be solved in the context of geographically-distributed heterogeneous multi-domain networks. In this context, Edge Datacenters have enabled network operators to place virtualized controller instances closer to users, besides providing more candidate locations for controller placement. In this study, we propose a dynamic controller placement method for optical transport networks that considers the heterogeneity of optical controllers, resource limitations at edge hosting locations, latency requirements, and costs. Simulation studies considering practical scenarios show significant cost savings and delay reductions compared to standard placement approaches.

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“…Recently, control plane architectures [12] [16] in T-SDN have been proposed to accommodate multiple heterogeneous network domains and associated domainspecific ON controllers. However, the design of T-SDN control plane architecture is still evolving and is an active research area [2] [16]. Ref.…”

Section: Background and Related Workmentioning

confidence: 99%

Virtualized controller placement for multi-domain optical transport networks using machine learning

et al. 2020

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Optical multi-domain transport networks are often controlled by a hierarchical distributed architecture of controllers. Optimal placement of these controllers is very important for efficient management and control. Traditional SDN controller placement methods focus mostly on controller placement in datacenter networks. But the problem of virtualized controller placement for multi-domain transport networks needs to be solved in the context of geographically-distributed heterogeneous multi-domain networks. In this context, Edge datacenters have enabled network operators to place virtualized controller instances closer to users, besides providing more candidate locations for controller placement. In this study, we propose a dynamic controller placement method for optical transport networks that considers the heterogeneity of optical controllers, resource limitations at edge hosting locations, and latency requirements. We also propose a machine-learning framework that helps the controller placement algorithm with proactive prediction (instead of traditional reactive threshold-based approach). Simulation studies, considering practical scenarios and temporal variation of load, show significant cost savings compared to traditional placement approaches.

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Robust hierarchical control plane for Transport Software-Defined Networks

Cited by 11 publications

References 15 publications

Reliable Control and Data Planes for Softwarized Networks

Reliable Control and Data Planes for Softwarized Networks

Virtualized Controller Placement for Multi-domain Optical Transport Networks

Virtualized controller placement for multi-domain optical transport networks using machine learning

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