SDN Controller Placement With Delay-Overhead Balancing in Wireless Edge Networks

Qin, Qiaofeng; Poularakis, Konstantinos; Iosifidis, George; Kompella, Sastry

doi:10.1109/tnsm.2018.2876064

Cited by 42 publications

(34 citation statements)

References 37 publications

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“…CP problem has been addressed by many existing proposals specifically for Data center networks [24], Large Scale SDN Networks [25], Software-defined wide area networks [26], Wireless Edge Networks [27]. In [24], the authors used a coalition formation games to form different clusters for stable network partitioning and localization of controllers.…”

Section: B Motivation and Research Challengesmentioning

confidence: 99%

Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud Supported FinTech Applications

Ren

Aujla

Jindal

et al. 2023

IEEE Internet Things J.

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Section: B Motivation and Research Challengesmentioning

confidence: 99%

Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud Supported FinTech Applications

Ren

Aujla

Jindal

et al. 2023

IEEE Internet Things J.

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“…In a multicontroller network, controllers communicate data with each other using a consensus protocol for synchronization. Based on [16], various consensus approaches may be employed by several controllers to attain synchronization goals. According to the presented structure, the leader-based case is discussed here.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

SDN Controller Deployment for QoS Guarantees in Tactical Ad Hoc Networks

Yan

Wen

2021

Wireless Communications and Mobile Computing

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Tactical ad hoc networks (TANET) accomplish the corresponding tasks via a hopeful device-to-device connection mechanism for data transmission and resource management without a centralized foundation. Software-defined networking (SDN) provides an evolution from the previous networks by decoupling the network control from data forwarding and providing a novel paradigm for network handling. Nevertheless, a SDN-based strategy in TANET leads to various novel problems since the primary construction cannot be employed in mobile ad hoc networks anymore. In this paper, a new SDN-based structure is constructed for TANET. Then, both delay and energy consumption (EC) are utilized to model the controller deployment and data-plane assignment problems, by which quality-of-service (QoS) guarantees are realized. Afterwards, mixed-integer programming (MIP) is adopted to solve the mentioned model. Finally, according to the experimental outcomes, it can be observed that the presented approach ensures the data-plane delay and optimizes the EC.

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“…Finally, hill climbing with simulated annealing was used to find an efficient solution. Qin et al in [32] studied the controller placement problem in edge networks. Since increasing the distance between controllers and nodes increases delay significantly, a scattered placement of controllers across the network would reduce both the distance and delay.…”

Section: Controller Placement In Wsnsmentioning

confidence: 99%

“…The more the number of controllers, the higher the value of this function. To calculate the value of this function for the different number of controllers, we use the flow table synchronization overhead introduced in (Figure 10 of [32]), where the inter-controller synchronization cost is investigated and classified into several types.…”

Section: Inter-controller Synchronization Costmentioning

confidence: 99%

“…In other words, for bigger networks, this limit increases. To calculate the value of this function, we use the results presented in (Figure 3b of [32]), where controller-node synchronization overhead for different sizes of the network is investigated. Apparently, if too many controllers are selected such that the inter-controller synchronization overhead exceeds SyncLimit(N), the synchronization overhead can hinder further improvement of the network performance.…”

Section: Inter-controller Synchronization Costmentioning

confidence: 99%

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Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

Tahmasebi

Safi

Zolfi

et al. 2020

Sensors

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Due to reliability and performance considerations, employing multiple software-defined networking (SDN) controllers is known as a promising technique in Wireless Sensor Networks (WSNs). Nevertheless, employing multiple controllers increases the inter-controller synchronization overhead. Therefore, optimal placement of SDN controllers to optimize the performance of a WSN, subject to the maximum number of controllers, determined based on the synchronization overhead, is a challenging research problem. In this paper, we first formulate this research problem as an optimization problem, then to address the optimization problem, we propose the Cuckoo Placement of Controllers (Cuckoo-PC) algorithm. Cuckoo-PC works based on the Cuckoo optimization algorithm which is a meta-heuristic algorithm inspired by nature. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. To evaluate the performance of Cuckoo-PC, we compare it against a couple of state-of-the-art methods, namely Simulated Annealing (SA) and Quantum Annealing (QA). The experiments demonstrate that Cuckoo-PC outperforms both SA and QA in terms of the network performance by lowering the average distance between sensors and controllers up to 13% and 9%, respectively. Comparing our method against Integer Linear Programming (ILP) reveals that Cuckoo-PC achieves approximately similar results (less than 1% deviation) in a noticeably shorter time.

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SDN Controller Placement With Delay-Overhead Balancing in Wireless Edge Networks

Cited by 42 publications

References 37 publications

Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud Supported FinTech Applications

Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud Supported FinTech Applications

SDN Controller Deployment for QoS Guarantees in Tactical Ad Hoc Networks

Cuckoo-PC: An Evolutionary Synchronization-Aware Placement of SDN Controllers for Optimizing the Network Performance in WSNs

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