On reliability improvement of Software-Defined Networks

Moazzeni, Shadi; Khayyambashi, Mohammad Reza; Movahhedinia, Naser; Callegati, Franco

doi:10.1016/j.comnet.2018.01.023

Cited by 40 publications

(22 citation statements)

References 18 publications

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“…At the end, the upper bound of R(G) is expressed by (11). 30 As our algorithm considers the topology of the clusters, if there is any intercluster link between two clusters, its failure probability is taken into account, too.…”

Section: The Methods To Calculate the Reliability Ratementioning

confidence: 99%

“…Moazzeni et al propose the "Reliable Distributed SDN (RDSDN)" method and a new formula to calculate the reliability rate of the subnetworks, based on the load and considering the number and degree of the nodes as well as the loss rate of the links. 30 The controller with the highest reliability rate is selected to be the coordinator of the whole network, which is responsible for detecting the crash failure of the other controllers and recovering the failure.…”

Section: Related Workmentioning

confidence: 99%

“…The master controllers calculate the reliability rate of their clusters, based on the number of nodes connected to a controller, and the number of the intracluster and intercluster links and their loss rates using the reliability rate formula, which we had presented in our previous RDSDN method recently. 30 The details of this formula with related proofs are described in the following.…”

Section: The Birdsdn Proposalmentioning

confidence: 99%

“…In contrast, it will create a new packet-out containing its reliability rate and name as payload and will send the packet to the switch connected to the other controller using the data plane network. The details of sending Packet-out messages and receiving Packet-In messages are provided in Moazzeni et al 30 Reliability out i:…”

Section: The Improved Coordinator Finder Algorithmmentioning

confidence: 99%

“…The time until failure of an SDN controller C can be determined by Weibull distribution as in Equation (16). 30 The Weibull distribution is a versatile distribution that has the ability to assume the characteristics of many different types of distributions. This has made it extremely popular among reliability engineers and quality practitioners, who have made it the most commonly used distribution for modeling reliability data.…”

Section: Reliability Evaluationmentioning

confidence: 99%

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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%

Section: Related Workmentioning

confidence: 99%

Section: The Birdsdn Proposalmentioning

confidence: 99%

Section: The Improved Coordinator Finder Algorithmmentioning

confidence: 99%

Section: Reliability Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

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

Moazzeni¹,

Khayyambashi

Movahhedinia

et al. 2020

Int J Communication

Self Cite

View full text Add to dashboard Cite

show abstract

References

2022

Software‐Defined Networking

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Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

Mohanty,

Sahoo

2024

Concurrency and Computation

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SummarySoftware‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions.

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On reliability improvement of Software-Defined Networks

Cited by 40 publications

References 18 publications

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

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

References

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

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