Reliability Optimization for Controller Placement in Software-Defined Networks

Martyna, Jerzy

doi:10.1007/978-3-319-59415-6_29

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In the reliability analysis of an SDN network, metrics such as its topology and the probability of failure of its components and links should be considered. 32 In this article, in order to calculate the reliability rate, each cluster is modeled by a probabilistic graph, G (N, D, Q l ), where N is the set of nodes; vector D, of size |N|, consists of the degrees of each node; and matrix Q l represents the failure probability of the links between any two nodes. We assume that this failure…”

Section: The Methods To Calculate the Reliability Ratementioning

confidence: 99%

Improving the reliability of Byzantine fault‐tolerant distributed software‐defined networks

Moazzeni¹,

Khayyambashi

Movahhedinia

et al. 2020

Int J Communication

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This paper presents a method to improve the reliability and fault tolerance of distributed software-defined networks. This method is called "BIRDSDN (Byzantine-Resilient Improved Reliable Distributed Software-Defined Networks)." In BIRDSDN, a group communication is implemented among the controllers of the whole clusters. This method can detect the crash failure and Byzantine failure of any controller and undertakes a fast detection and recovery scheme to select the controllers to take over the orphan switches.BIRDSDN takes into account the reliability of the nodes considering the failure probability of intracluster and intercluster links, topology, load, and latency.The numerical results show that this approach performs better than the other approaches regarding failure detection, recovery, latency, throughput, reliability, and packet loss. K E Y W O R D S

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Section: The Methods To Calculate the Reliability Ratementioning

confidence: 99%

Improving the reliability of Byzantine fault‐tolerant distributed software‐defined networks

Moazzeni¹,

Khayyambashi

Movahhedinia

et al. 2020

Int J Communication

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“…Zhong et al used “min‐cover” algorithm to minimize the average number of switches losing connection to the controller due to link failure as a criterion for solving RCPP 13 . In another study, 14 the backtracking method was used for solving the RCPP. Schütz et al focused on the k ‐cover method to solve RCPP, in which, each switch must be covered by at least k controllers via disjoint paths 15 .…”

Section: Related Workmentioning

confidence: 99%

Reliable controller placement in software‐defined networks using reliability covering graph and irregular cellular learning automata

Gholamrezaei

Mirjalily

Emadi

2021

Int J Communication

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Summary Software‐defined networking is a new paradigm in network management that has received much attention in recent years. One of the important challenges in designing software‐defined networks (SDNs) is the reliable controller placement problem (RCPP) that is the problem of finding the number and locations of controllers to satisfy the reliability requirements. The high complexity of exact reliability measurement led researchers to use estimation techniques that results in overestimating the number of controllers and incorrect placement. In this paper, we introduce a new concept named reliability covering graph (RCG) that enables us to use the exact reliability measurement (ERM) methods in solving the RCPP with desired level of complexity. To construct the RCG, for each candidate node, a subgraph with the adjustable radius r is extracted from the network graph. In the next step, the RCG is developed by calculating the exact reliability between the central node and any other nodes in the resulting subgraphs. In the last step, the RCG is converted to a machine learning tool named irregular cellular learning automata (ICLA) that determines the number and locations of controllers as well as the switches they cover. To study the efficiency of the proposed method, its performance is compared with the optimal results obtained from the ILP solver. The simulation results of 800 case studies on 100 standard topologies show that the average difference in results is 0.00125. Thanks to our innovative structure RCG, the number of controllers required is significantly reduced compared to previous methods.

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Reliability Optimization for Controller Placement in Software-Defined Networks

Cited by 2 publications

References 11 publications

Improving the reliability of Byzantine fault‐tolerant distributed software‐defined networks

Improving the reliability of Byzantine fault‐tolerant distributed software‐defined networks

Reliable controller placement in software‐defined networks using reliability covering graph and irregular cellular learning automata

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