Sharing data store and backup controllers for resilient control plane in multi-domain SDN

Li, Jiacong; Wang, Ying; Li, Wenjing; Qiu, Xuesong

doi:10.23919/inm.2017.7987315

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Ros and Ruiz [113] proposed heuristic solutions to ensure that the probability of having an operational path is either nonzero or above a threshold in the event of a failure. Several meta‐heuristic solutions [89, 90, 124] based on NSGA‐II, PSO, and simulated annealing have also been proposed.…”

Section: Cpp Solution Methodsmentioning

confidence: 99%

“…A particle is generally used to represent the placement instance of the controllers [44, 117–119]. The set of the switches are eventually partitioned into k clusters or backup instance of controllers [90]. Pareto simulated annealing (PSA) which has evolved from simulated annealing is also an established technique among researchers [84, 85] to solve the multiobjective CPP.…”

Section: Cpp Optimization Strategiesmentioning

confidence: 99%

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Controller placement problem in software‐defined networking: A survey

Kumari

Sairam

2021

Networks

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A distinctive feature of software‐defined networking (SDN) is a logically centralized control plane realized using multiple physical controllers. The placement of the controllers, the so‐called controller placement problem (CPP), is a crucial design issue. It influences network performance parameters such as latency, flow setup time, network availability, load balance of the controllers, and energy consumption. In this article, we illustrate the formulation of these CPP objectives. We categorize the CPP design solutions as either static or adaptive. In adaptive CPP, the solutions proposed dynamically adapt to the number of controllers required and the switch to controller mapping to varying network traffic. We further differentiate adaptive CPP as wired or wireless. The optimization strategies adopted by the papers are analyzed and grouped into five categories: exact, heuristic, meta‐heuristic, clustering, and game theory. The merits and demerits of each approach are discussed. In conclusion, we outline the research challenges worth investigating.

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Section: Cpp Solution Methodsmentioning

confidence: 99%

Section: Cpp Optimization Strategiesmentioning

confidence: 99%

Controller placement problem in software‐defined networking: A survey

Kumari

Sairam

2021

Networks

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“…Tanha et al focused on connecting each switch to more than one controller to increase resiliency by considering the delay and capacity of the controllers, where Maximal Clique is their suggestion for solving the problem 33 . In a similar attempt 34 that is based on PSO algorithm, a shared data repository was used so that controllers could retrieve the up‐to‐date information after recovery.…”

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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“…Работы [12,13] Вопрос надежности ПКС при выходе из строя контроллеров рассматривается в работе [15]. Задачу оптимизации числа и мест расположения резервных контроллеров авторы предлагают рассматривать как мультикритериальную задачу комбинаторной оптимизации и решают ее при помощи МРЧ и генетических алгоритмов.…”

Section: проблема размещения контроллеров и критерии оптимизацииunclassified

A Survay on the Swarm Intelligence Approaches to Controller Placement Problem in the Software Defined Networks Design and Optimization

Vybornova

2020

Inf. tehnol. telekommun.

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This article is dedicated to the overview and analysis of the existing approaches to the controller placement problem for the multi-controller Software Defined Networks (SDN) with a focus on the swarm intelligence methods. For this purpose, the controller placement problem is defined, as well as possible optimization functions and constraints. Research subject of this article is a controller placement problem for the multi-controller SDN. As a research method a comparative analysis is used. Core results of the work is a comparison of different approaches based on the different optimality criteria and constraints. Practical relevance of the work is that the results can be used in the SDN design and optimization.

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Sharing data store and backup controllers for resilient control plane in multi-domain SDN

Cited by 9 publications

References 13 publications

Controller placement problem in software‐defined networking: A survey

Controller placement problem in software‐defined networking: A survey

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

A Survay on the Swarm Intelligence Approaches to Controller Placement Problem in the Software Defined Networks Design and Optimization

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