Chaotic salp swarm algorithm for SDN multi-controller networks

Ateya, Abdelhamied A.; Muthanna, Ammar; Vybornova, Anastasia; Algarni, Abeer D.; Abuarqoub, Abdelrahman; Koucheryavy, Yevgeni; Koucheryavy, Andrey

doi:10.1016/j.jestch.2018.12.015

Cited by 83 publications

(64 citation statements)

References 40 publications

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“…SDN is a networking paradigm that emerged in recent years via several initiatives and standards to ensure network flexibility, efficient utilization, cost-effectiveness, and innovation [1][2] [3]. Its design principle [4] is characterized by separating the control plane from the data plane through the provision of network programmability which brings about network dynamic configuration, control, and management simplicity [1] [5] [6].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, several SDN protocols have been developed such as ForCES, OpenFlow, HyperFlow, etc. with ForCes and OpenFlow being the most commonly implemented southbound protocols [1] [2] [3]. ForCes was initially declared the most efficient and dynamic southbound interface in the SDN as compared to OpenFlow.…”

Section: Introductionmentioning

confidence: 99%

“…However, having a single physically or logically centralized controller in charge of the forwarding nodes in a large-scale network constitutes a serious bottleneck to the network. It poses as a single point of failure that could leave the network with no intelligence, poor efficiency, unexpected high delays due to distance between controller and switches, lack of scalability support for large SDN networks, low controller processing power [1][2][3][6][7] [10]. These adversely impact the network performance and significantly limit the reliability, and availability of the entire networks [3] [11].…”

Section: Introductionmentioning

confidence: 99%

“…Also, the overall reliability increases as the failure of one controller do not inhibit the network operations, making the network more efficient and scalable [1][5] [7] [9]. Despite these benefits, multiple controllers also bring about several challenges where optimal controller placement problem (CPP) is a typical issue [1][2][3][4][5][6][7][8][9][10]. 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].…”

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Thus, enabling a simplified networks management. One of the main factors affecting this evolution is introducing the concept of separation between network control logic (control plane) from the underlying hardware (data plane) to achieve abstraction of lower-level functionality [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Review of the Control Plane Scalability Approaches in Software Defined Networking

Abuarqoub¹

2020

Future Internet

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Recent advances in information and communications cloud-based services hold the potential to overcome the scalability and complex maintenance limitations of traditional networks. Software Defined Networking (SDN) surfaced as a promising paradigm to mitigate such limitations while offering flexible networks management. Particularly, SDN separates the control plane from the data plane to achieve abstraction of lower-level functionality, hence, allowing more efficient network management and utilization. However, SDN suffers from various performance and scalability problems leading to significant research efforts on maximizing the scalability of the control plane. This paper aims at reviewing different SDN controller scalability, topology-based and mechanism-based approaches, as well as discussing and analyzing how they attempt to solve the scalability challenge. Furthermore, this paper elaborates on the promising research trends and challenges. Our insights are also discussed to stimulate further research efforts addressing the control plane scalability in SDN.

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Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources

Hassan

Elsayed

Kamel

et al. 2022

Intl J of Energy Research

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Summary During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs.

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Chaotic salp swarm algorithm for SDN multi-controller networks

Cited by 83 publications

References 40 publications

Comprehensive Review of SDN Controller Placement Strategies

Comprehensive Review of SDN Controller Placement Strategies

A Review of the Control Plane Scalability Approaches in Software Defined Networking

Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources

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