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Controller placement issue (CPI) in software-defined networks (SDNs) describes the controllers' number, location, and assigning of forwarding devices to controllers. Recently, mathematical formulations and algorithms have been proposed to solve various problems in SDNs and software-defined wide area networks. The comprehensive literature review can be divided into four groups according to objectives: (i) minimizing latency between forwarding devices or switches and their corresponding controllers, and minimizing latency between controllers, (ii) improving network resilience and stability, (iii) minimizing energy consumption and installation costs and (iv) using multi-objective approaches. In addition to the objectives of each research, the importance of this paper is to examine the CPI in terms of reducing the network search space in order to optimally place the controller and how to assign switches to the controllers. In this paper, first the mathematical formulations in previous studies will be examined and then, for solving CPI, the existing algorithms will be discussed. Different classifications of CPIs and related formulas/algorithms, descriptions, advantages and disadvantages will be separately provided. A comprehensive comparison of proposed approaches with their advantages and disadvantages in the summarized tables will be provided. Also, a comparative discussion of different statistics of CPIs will be presented in terms of some technical features such as objective-oriented problems and parameters in four categories, estimated environments, and efficient estimating factors in CPIs. Finally, we explained the future studies' challenges, problems related to CPIs, ideas and following orientations in this field. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a network architecture where the control and data plane are separated. As the network size grows, relying on just one controller can lead to various problems. Thus, in highly scalable networks, multiple controllers are needed. This critical issue of determining the number and placement of controllers is known as the controller placement problem (CPP). In this paper, game theory is used to solve CPP by identifying the optimal number of controllers. Two algorithms, golden eagle optimization (GEO) and grey wolf optimization (GWO), are utilized to find the most efficient mapping between switches and controllers. Since CPP is a discrete problem, GEO and GWO have first been discretized and then hybridized to form a new algorithm called GEWO. This algorithm is used to discover the most efficient mapping between switches and controllers. Additionally, simulated annealing is employed for better local search. The effectiveness of this approach is evaluated using different numbers of controllers on four well-known software-defined networks from the Internet Topology Zoo. The results are compared against various existing algorithms in the field, and it is observed that GEWO outperforms the competition. The findings demonstrate that GEWO reduces load imbalance by 24.07%, decreases end-to-end delay by 20.95%, and lowers average energy consumption by 11.65%. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are considered as the foundation of the Internet of Things. Inherent problems in wireless sensor networks such as power consumption, lack of flexibility, and disability in development and programming have led to serious challenges in these networks. Software-defined networking (SDN) is flexible with development and programming capabilities that decouple the control and data planes. The combination of wireless sensor networks and software-defined networks has created the idea of software-defined wireless sensor networks (SDWSNs). Security is considered as one of the most fundamental issues in any network. Due to their combinatorial nature, the software-defined wireless sensor networks faced a variety of security challenges for both wireless sensor networks and software-defined networks. This paper proposes a novel architecture with an unsupervised intrusion detection algorithm using a hierarchical approach to improve the security of integrated software-defined wireless sensor networks. In the proposed architecture, the sensors are not fully dependent on the SDWSN controller; instead, they run the appropriate intrusion detection algorithm module locally at the layer. The data analysis results in different zones, produced by clustering based on entropy and cumulative point similarity as criteria, are sent to the SDWSN controller, and decisions are made after the final check of data normality or abnormality. To examine the effectiveness of the proposed architecture and algorithm, the sensors were simulated on Cooja, WSN-DS and NSL-KDD standardized datasets. The results show that the proposed method is able to detect the abnormal traffic up to 97%. [ABSTRACT FROM AUTHOR]

According to a study on advanced technology, resource planning, and design for Fifth Generation (5G) architecture and elsewhere, network traffic classification is a basic and complex part of software-defined networking (SDN). 5G requires end-to-end security that uses its software-defined architecture to automatically monitor the network and classify the traffic flows. To improve the security in network traffic classification, it is necessary to apply a correct set of policy rules to classify future network traffic flows. To create a communication-efficient and intelligent traffic classification framework in the SDN environment, machine learning is used between the data and the control planes. The proposed ensemble learning pre-processing tool to categorize incoming VPN traffic by applying a possible set of policies. The proposed technique is compared with existing classifiers and has a higher accuracy of 98% to 99.9% for ensemble models than single classifiers and other existing options, according to an evaluation of performance. [ABSTRACT FROM AUTHOR]

As the growing of data volumes due to the successive development of new mobile devices and the creation of new applications, the emergence of multi-access edge computing can successfully improve quality of service based on reduced latency and lower system energy consumption. The introduction of software-defined networking technologies in multi-access edge computing environments supports access to more network devices and enhances the scalability and service management flexibility of mobile edge computing environments. The limited nature of computing resources in mobile edge computing environments makes resource management a critical issue. Therefore, to minimize the energy consumption and latency of task execution in mobile edge computing environment, and to ensure reasonable resource allocation during task execution, a resource management strategy based on multi-objective optimization in edge computing environment is proposed. In this strategy, the overall energy consumption weighting and minimization problem is solved by optimizing the management of communication and computing resources, and an improved NSGA-II algorithm is proposed to rationally allocate communication and computational resources for each task. To deal with load imbalance caused by large traffic fluctuations in multi-access edge computing environments based on software-defined networks, in this paper, a load-balancing-oriented switch migration strategy is proposed in which a switch migration algorithm based on an improved ant colony algorithm is proposed to optimally select the switch migration process so that the static deployment of the controller adapts to the changing needs of dynamic flows in the network. Experimental results demonstrate that the proposed resource management strategy minimizes the latency and energy consumption during task execution and increases resource utilization and average throughput of servers. The proposed switch migration strategy can effectively achieve load balancing and reduce the response time. [ABSTRACT FROM AUTHOR]

Software-defined networks have many benefits such as more control over the control plane and reduced operating costs through separating the control plane from the data plane in network equipment. One of the most critical problems in software-defined networks is a controller placement problem, which significantly influences its overall performance. The purpose of this problem is to determine the number of controllers required and how to assign switches. This paper attempts to solve this problem, aiming to reduce the network's operational cost and to improve their survivability and load balancing. Hence, we have tried to divide the network into several subdomains using segmentation. Then, we used multi-criteria decision-making methods to solve the controller placement in each subdomain. For this purpose, we considered criteria such as reliability rate, cost, delay, and processing capacity. To assign switches to controllers, we used the proposed mathematical model to minimize the objective function while observing the defined constraints. Furthermore, according to the proposed architecture, we used distance to the central controller, adjacent neighborhood, and controller memory to select the cluster head controller for each subdomain to communicate with the central controller. Finally, we performed experiments on topologies Uran, SwitchL3, and Sinet of the Internet Topology Zoo to evaluate the proposed method. In these experiments, we compared the results of the proposed method with our work-related methods, including Controller Placement Analytic Hierarchy Process (CPAHP) and Controller Placement Modified Density Peak (CPMDP), in terms of criteria such as cost, survivability, number of controllers, connection failure probability, average delay, and controller load-balancing rate. The results show that the proposed method outperforms CPMDP and CPAHP. Thus, the proposed method has a 24.97% and 19.76% improvement in reducing network implementation costs than CPMDP and CPAHP, respectively. [ABSTRACT FROM AUTHOR]

This research presents the detection and mitigation of distributed denial of service (DDoS) in software defined networks (SDN). The proposed method consists of three modules: classifier module, mitigation module, and collaborative module. An ensemble classifier called V-NKDE is capable of detecting DDoS attacks accurately. The mitigation module blocks malicious traffics and purges entries of malicious traffic from the switch flow table. The collaborative module shares DDoS detection and mitigation rules among multiple SDN controllers using Redis Simple Message Queue mechanism. The proposed classifier performance validation on InSDN2020, CICIDS2017, NSL-KDD and UNSW-NB15 datasets. Furthermore we evaluated our proposed classifier in real traffic on an SDN simulation tested. The results show that the proposed method can detect DDoS attacks with high accuracy using an ensemble classifier, which performs better than single classifiers. More importantly, the false positive rate is greatly reduced, showing detection and mitigation of DDoS attacks across multi-controller domains with low controller overhead. [ABSTRACT FROM AUTHOR]

Due to the development of the Internet and the smart end systems such as smartphones, portable laptop, and other smart mobile devices, as well as the emergence of concepts such as cloud computing, social networks, and Internet of Things, 4G and 5G have changed network requirements. A software-defined network (SDN) is a new architecture to support new network requirements. This architecture is composed of three layers, data, control, and application plane. A lot of papers have been published in well-known journals to improve network performance, so we classify the solutions for performance improvement based on three SDN layers and three SDN-based spheres which are (a) SDN-data center, (b) wireless networks, and (c) software-defined wide area network. This paper surveys the articles which have been published by Springer, IEEE, Elsevier, and ACM. We extract the performance improvement solutions and resources which are required for performance control and the metrics to evaluate the network performance. This article can help network enthusiasts to better understand, investigate, and improve the performance of SDN-based networks. [ABSTRACT FROM AUTHOR]

The networking infrastructure of a software-defined network (SDN) requires further study to achieve continuity and high availability of data transactions for cloud computing services. However, various types of failures on links or system components are encountered because of high-speed and complicated structures of hosts and network devices. This study examines the specific characteristics and impact of various failures on a typical SDN infrastructure. We propose a stochastic model using stochastic reward net by incorporating hardware failures (of hosts, switches, storage, and links) and software failures [virtual machines (VMs)]. The system model is analyzed based on steady-state availability under default parameters. Comprehensive sensitivity analyses are conducted to study the system behaviors with respect to different major factors of impact. A reliability analysis is also conducted to pinpoint the role of VM migration in extending the system lifetime. Furthermore, operational cost is analyzed in detail to identify the main sources and to observe the effect of major system parameters on costs over time. The analytical results are verified by a developed discrete-event simulation module based on CloudSim Plus simulation open-source package. This study provides a helpful basis for network design and implementation in SDN infrastructures. [ABSTRACT FROM AUTHOR]