A Multi-Controller Placement Strategy Based on Delay and Reliability Optimization in SDN

Fan, Zifu; Yao, Jie; Yang, Xianhui; Wang, Zhengqiang; Wang, Xiaoyu

doi:10.1109/wocc.2019.8770551

Cited by 20 publications

(18 citation statements)

References 15 publications

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“…The research work in [26] considered the trade-off among various performance requirements, such as load balancing, failure tolerance, and transmission latency, and in order to achieve the trade-off, an optimal controller placement framework based on the Pareto game model is proposed. The authors in [27] considered the delay between controller and the associated switches in link failure state and proposed a particle swarm optimization-based heuristic controller placement method to guarantee the delay and reliability in control plane.…”

Section: Delay Minimization-based Controller Placement Algorithmsmentioning

confidence: 99%

“…While the authors in [20][21][22][23][24][25][26][27] stressed control plane delay optimization, they mainly considered a relatively simple delay formulation and ignored some important delay components, such as the signaling transmission delay, signaling processing delay, and the inter-controller delay. Furthermore, most of the previous research works assumed that the controllers to be deployed are of exactly the same characteristics which can be arbitrarily set; however, in practical deployment scenarios, the characteristics of the controllers may be subject to certain constraints and the parameters of the controllers might be pre-defined in a certain parameter set, in which case, the problem of capacitated controller placement or joint controller placement and capacity matching should be studied.…”

Section: Delay Minimization-based Controller Placement Algorithmsmentioning

confidence: 99%

“…Otherwise, the label having not being allocated by the distribution H is selected in G l 0 . Set S = V 1 , and T = ψ, which denotes the empty set (27) and replace the existing labeling l with l * by…”

Section: Find An Initial Feasible Vertex Labeling and Determinementioning

confidence: 99%

“…For instance, it is highly possible that the controllers can only be selected from a given candidate controller set, and the parameters of the controllers are also pre-defined in a certain parameter set. In this case, the capacitated controller placement problem, i.e., determining the number, location, and capacity of controllers based on a candidate controller set, should be considered [25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Control plane delay minimization-based capacitated controller placement algorithm for SDN

Chai

Yuan

Zhu

et al. 2019

J Wireless Com Network

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Software-defined networking (SDN) is proposed as a new network paradigm which decouples control plane from data plane and provides flexible network management. In this paper, we consider the capacitated controller placement problem in SDN, which jointly determines the number, location, and the capacity matching strategy of SDN controllers. redTo stress the importance of control plane delay which is composed of both the transmission and processing delay between controllers and switches, and the inter-controller delay; we formulate control plane delay minimization problem subject to controller-switch association constraints, controller capacity constraints, etc. Since the formulated optimization problem is a complicated nonlinear integer programming problem which cannot be solved easily, we transform it into controller-switch association subproblem and controller capacity matching subproblem. To solve the controller-switch association subproblem, we propose a heuristic algorithm based on Dijkstra algorithm and K-means algorithm. Given the controller-switch association strategy, we then apply the Kuhn-Munkres (K-M) algorithm to solve the controller capacity matching subproblem and obtain the capacitated controller placement strategy. Simulation results are shown to demonstrate the effectiveness of the proposed algorithm.

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Section: Delay Minimization-based Controller Placement Algorithmsmentioning

confidence: 99%

Section: Delay Minimization-based Controller Placement Algorithmsmentioning

confidence: 99%

Section: Find An Initial Feasible Vertex Labeling and Determinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Control plane delay minimization-based capacitated controller placement algorithm for SDN

Chai

Yuan

Zhu

et al. 2019

J Wireless Com Network

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show abstract

“…A recent survey of the literature shows that many works seek to provide greater reliability in terms of availability and performance for SDN-based ecosystems. However, most studies only concentrate on higher-level solutions and rely on NB-APIs provided by the controller to develop applications that seek to optimize the control plane through particular strategies, such as load balancing [18], controller placement [19], and network orchestration [20].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Orchestration and Infrastructure Programmability in SDN With NOTORIETY

et al. 2020

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Software-Defined Networking (SDN) controllers are nowadays expected to manage large infrastructures and services in a practical, efficient, and optimized way. To achieve such control, SDN networks should increase the automation of orchestration functions to fullfil both service and network management lifecycles. However, network management applications have stringent resource demands that orchestrators need to honor. Most of the available orchestrators focus on high-level approaches that rely exclusively on the Northbound API (NB-API). A few strategies have also been designed to optimize the controllers' internal mechanisms through features enabled by the Southbound API (SB-API). However, such solutions do not focus on optimal information delivery to the orchestrator, a crucial property to provide prompt feedback in response to network events, and to potentially drive self-healing and autoscaling properties. To address such need, we present NOTORIETY, an orchestration system that provides an abstraction for real-time SDN network controller event message handling. NOTORIETY is able to maximize the orchestration capabilities of SDN controllers including innovative features for processing and delivering network event information. NOTORIETY's design consists of mechanisms that empower SDNcontrolled entities by applying filtering rules to efficiently control and optimize data flows provided by the SDN controller. We implemented a testbed to assess our NOTORIETY proposal following the guidelines provided in the RFC 8456, and we benchmark its performance over three SDN controllers (OpenDaylight, ONOS, and Floodlight). The evaluation results reveal the effectiveness of NOTORIETY in reducing the execution time of SDN requests, load processing, and overall data marshaling volume in several scenarios.

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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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A Multi-Controller Placement Strategy Based on Delay and Reliability Optimization in SDN

Cited by 20 publications

References 15 publications

Control plane delay minimization-based capacitated controller placement algorithm for SDN

Control plane delay minimization-based capacitated controller placement algorithm for SDN

Enhancing Orchestration and Infrastructure Programmability in SDN With NOTORIETY

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

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