An adaptive heuristic for multi-objective controller placement in software-defined networks

Ahmadi, Vahid; Khorramizadeh, Mostafa

doi:10.1016/j.compeleceng.2017.12.043

Cited by 46 publications

(38 citation statements)

References 11 publications

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“…Generally, most CPP is modeled as a graph, G = (V, E, U) topology where V is the set of n switches, E is the set of edges (physical links) among switches or controllers and U is the set of k controllers [15]. Studies on CPP are centered on designing techniques to solve for the value of k and the relation given by U → V, which for instance, is the shortest path latencies between each pair of nodes when minimizing latency as the only objective in the objective function [12] [15]. The formulation is an optimization problem that is either data-driven [16] or metric-driven formulated to find the minimum or maximize cost, the optimal number of controllers, switch-controller (SC) latency, controller synchronization time or hybrid metrics or multi-objective optimization problem about controller location or placement [1].…”

Section: The Controller Placement Problemmentioning

confidence: 99%

“…In general, during CPP that involves identifying the required number of controllers and their locations, the key considerations are the performance metrics [5][10] [12]. The metrics are used to evaluate the quality of the different controller placement in the network and are critical to its performance, QoS, efficiency, scalability, and reliability [5][10] [12].…”

Section: F Inter -Controller Communicationmentioning

confidence: 99%

“…Simulation results showed efficiency in the CPP and optimal assignment as well as the outperformance of latency-based assignment by multi-criteria assignment in terms of load balancing. In [12], Ahmadi and Khorramizade proposed a large-scale multi-objective controller placement approach using a fast and efficient adaptation of evolutionary algorithms. They utilized a heuristic algorithm known as Multi-Start Hybrid Non-Dominated Sorting Genetic Algorithm (MHNSGA) while considering performance metrics such as SC-latency, CC-latency, load balancing, and reliability for link failures in the objective function.…”

Section: ) Genetic Algorithm Based Cppmentioning

confidence: 99%

“…For instance, to ensure network scalability, it is insufficient to merely increase the number of controllers or randomly placing the controllers anywhere as a satisfactory performance can't be achieved [7]. This means multiple controllers have to be properly placed in appropriate locations to meet several requirements and this action involved network partitioning [1] [9] [12]. Nevertheless, partitioning the network into multiple control domains to achieve good network performance can introduce several challenges than anticipated in terms of reliability, load balancing, latency, computation time, etc.…”

Section: Introductionmentioning

confidence: 99%

“…[1] [9]. It is against this backdrop that network engineers are faced with the CPP conundrum, prompting the search for answers to critical questions such as how to identify a minimum number of controllers, how to partition the network, where to locate each controller, and how to allocate controllers to switches and so on [1][2][3][5] [8][9] [12][13][14]. Seeking answers to these critical questions make multiple controllers' deployment for largescale network management and its policies enforcement an important research area [1] [3].…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Review of SDN Controller Placement Strategies

et al. 2020

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Software-Defined Networking (SDN) is a network paradigm introduced to overcome the inherent challenges of traditional networks. Its architecture is either deployed with a single controller or multiple controllers. While the first is not suitable for large-scale networks, the latter is confronted with a controller placement problem (CPP) in a large-scale network environment. CPP involves the challenge of deploying the optimal number of controllers within a network while meeting certain performance requirements considered conflicting in nature such as reliability, load balancing, latency, energy efficiency, and computation time. A single optimal or random placement may not be feasible in CPP and careful planning is of the essence to find an appropriate trade-off among the metrics. To achieve this, several CPP approaches have been proposed, developed, and deployed over the years, each having its unique objectives, strengths, and weaknesses. Therefore, this paper performed a comprehensive review of some of the existing approaches to identify the unique solutions offered, comprehend the different strategies and the challenges that exist as well as provide researchers with future directions aimed at improving the optimum location and allocation of controllers, in particular, for SDN application in wireless sensor network (WSN). The findings revealed several existing solutions and algorithms as well as several challenges such as the need for an efficient algorithm, attack-aware, cost-aware, and energy-aware CPP schemes while achieving a good quality of service.

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Section: The Controller Placement Problemmentioning

confidence: 99%

Section: F Inter -Controller Communicationmentioning

confidence: 99%

Section: ) Genetic Algorithm Based Cppmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comprehensive Review of SDN Controller Placement Strategies

et al. 2020

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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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Learning‐based multi‐constraint resilient controller placement and assignment insoftware‐defined networksusing covering graph

Gholamrezaei

Mirjalily

Emadi

2023

Trans Emerging Tel Tech

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The controller placement problem is one of the most important issues in designing software-defined networks (SDNs). In this context, one of the major challenges is the problem of multi-constraint resilient controller placement and assignment (MCRCPA). In this article, considering the main criteria for solving the problem of MCRCPA, we propose a learning-based approach named Lb-MCRCPA, which includes a wide range of constraints including resiliency, switch-to-controller delay, switch-to-controller reliability, inter-controller delay, and controller capacity in both main and backup modes. In the proposed method, we use an innovative structure called constraint covering graph (CCG) to calculate the reliability and to apply the set of constraints. The CCG is converted into a distributed learning model called irregular cellular learning automata (ICLA). The rules embedded in ICLA, together with the proposed assignment algorithm, can accurately determine the location of controllers. The performance evaluation of the proposed method for about 2600 different cases on more than 20 standard topologies of different sizes, not only proves its superiority over other methods but also proves its near-optimality compared to the optimal solution.

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An adaptive heuristic for multi-objective controller placement in software-defined networks

Cited by 46 publications

References 11 publications

Comprehensive Review of SDN Controller Placement Strategies

Comprehensive Review of SDN Controller Placement Strategies

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

Learning‐based multi‐constraint resilient controller placement and assignment insoftware‐defined networksusing covering graph

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