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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) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations. [ABSTRACT FROM AUTHOR]

To solve the problem of multi-SDN controller load balancing, a low-latency controller load balancing switch migration algorithm is proposed, and a load balancing framework consisting of three modules of load monitoring, decision-making, and switch migration is designed. Migrate the switch with the highest request rate to the controller with stronger processing capacity and closer distance, and achieve load balancing through multiple iterations, effectively solving the problem that the static controller deployment scheme cannot cope with the dynamic network environment. Since the current research on computing offloading does not consider the stability of the MEC system, an offloading decision-generation algorithm based on Lyapunov optimization is proposed. This algorithm designs a task queue scheduling model to transform the system stability problem into a queue backlog problem, considering the profit of the edge server and the delay of task processing, and establishing a resource optimization model to maximize the profit of the MEC system under the premise of meeting the stability and delay requirements of the MEC system. The experimental results show that the proposed controller load-balancing algorithm can speed up the load-balancing process and reduce the average response delay of the system by about 22.1% while maintaining high throughput. The proposed computing offload algorithm can reduce the average delay of the system by 52%, better allocate computing tasks, and make the edge server obtain higher profits. [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]

Investing in cybersecurity is increasingly considered a significant area and aspect a business or organisation should seriously consider. Some of these security solutions are network-based and provide many levels of protection. However, traditional networks are seen to be vendor-specific and are limited, enabling minor to no network flexibility or customisation. Implementing SDN to combat cyberattacks is a workable option for resolving this traditional network constraint. Less attention has been paid to how SDN has been utilised to address security concerns, with most surveys concentrating on the security challenges the SDN paradigm faces. This study aims to provide a comprehensive overview of the state-of-the-art on how SDN has been used to combat attacks between 2017 and 2022 by highlighting the specifics of each literature, its advantages, limitations, and potential areas for further study. This work introduces a taxonomy highlighting SDN's fundamental traits and contributions as a defence mechanism (SaaDM). [ABSTRACT FROM AUTHOR]

Nowadays, the growth of internet users has led to a significant increase in identity fraud security risks. One of the common forms of identity fraud is the Address Resolution Protocol (ARP) spoofing attack. These cyber-attacks come from ARP vulnerabilities and consist of compromising the victims' ARP caches by inserting fake IP-MAC pairs. These attacks should be tackled seriously because they can be used to launch more dangerous ones, such as denial of service or man-in-the-middle attacks. Most existing approaches against ARP spoofing attacks use a detection threshold to detect attackers in the network. However, these approaches may be ineffective against an intelligent attacker who avoids exceeding the threshold by combining spoofed ARP packets with normal ones. To address this problem, we leverage the advantages of software-defined networks to propose a game-theoretic approach that predicts the defender's best moves based on the Nash strategies. This approach is modeled as a non-cooperative game between the attacker who wants to poison victims' ARP caches, and the defender whose goal is to avoid ARP cache poisoning. The proposed method results in a mixed-strategy Nash equilibrium that identifies the best defensive strategy. It includes a player utility-based algorithm to detect malicious users and block their traffic or redirect them to a honeypot. Simulation results show that the proposed method is more suitable to ensure system security by preventing, detecting, and recovering from ARP spoofing attacks than those proposed in the literature. [ABSTRACT FROM AUTHOR]

Modern networks urge agility, flexibility, and capacity to cope with the growing demand for media content and applications increasingly oriented toward data consumption. The Central Offices (CO) of telecommunication providers, being a vital aggregator of different access networks, such as optical and mobile, need to be prepared to deal with these demands. The Open Broadband-Broadband Access Abstraction (OB-BAA) architecture fits into the initiative to modernize the Information Technology (IT) components of broadband networks, more specifically the COs. This paper discusses the development of a Virtualized Network Function (VNF) in the context of network security to be integrated as a component of an OB-BAA architecture guided by the Software-Defined Network paradigm. More specifically, the authentication and authorization of network equipment within the IEEE 802.1X protocol are applied to Next Generation Passive Optical Networks. The VNF development is based on the Golang language combined with gRPC programmable interfaces for communication between the various elements of the OB-BAA architecture, and then the components were “containerized” and inserted in the Docker and Kubernetes virtualization frameworks of a multinational telecommunications operator. Finally, performance metrics such as computational resource usage (CPU, memory, and network I/O) and execution time of VNF processes were analyzed in usage tests with multiple supplicants and distinct operational modes, to attest to the most promising virtualization scenarios. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) comprises the interconnection of things or objects that are embedded with both hardware and software. The WSN is a tiny sensor with end device sensors that are connected to the Internet. To perform effective routing in the WSN efficient and reliable data collection, schemes are deployed with the routing protocol for low power and lossy networks (RPL) routing scheme for the low power and lossy network. The RPL routing scheme of the low and lossy routing protocol design for the network with the objective function. The objective function in RPL routing involved network construction and maintenance through hop count. The RPL scheme uses the destination-oriented directed acyclic graph (DODAG) with the greedy election for estimation of instability in the network. The routers in the WSN are enabled with the software-defined network (SDN) server node. The process of routing comprises detection of routes between the source and the destination. This paper focused on secure routing and monitoring schemes in WSN. To improve the secure routing process in WSN, this paper developed a deep RPL-software-defined network (DRPL_SDN). The DRPL_SDN concentrated on the parent selection through RPL based on the predicted energy level of the parent node. The prediction is performed with the DRPL_SDN-based reinforcement learning method with the estimation of child count through a partial stability routing mechanism. The secure prediction is performed through the deep reinforcement learning method in DRPL_SDN for the succeeded node count for the routing stability. The security model is evaluated with the utilization of the knowledge discovery in database (KDD) dataset. With the KDD dataset, the different attacks are evaluated in the proposed DRPL_SDN model. Additionally, the proposed DRPL_SDN exhibits better load balancing with the uncontrolled node in the network. The DRPL_SDN focused on the establishment of a link in the available network path through a dynamic controlled environment. The simulation analysis expressed that DRPL_SDN achieves the minimal packet loss of 236 and the energy consumption is minimal for 6%. The simulation examination expressed that the DRPL_SDN model exhibits the ~ 13% higher performance than the RPL and ELDR. [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]

The IoT network is unique due to heterogeneous IoT nodes and resource-constrained devices; the approach for securing IoT networks needs to be different from the security measures implemented for traditional network communication. In IoT networks, various security vulnerabilities are exploited by an attacker to generate a variety of DDoS attacks. In this paper, the authors propose a unique approach for securing IoT networks using an SDN-enabled framework that incorporates a dynamic counter-based approach and deep learning models. The aim is to detect and mitigate various security vulnerabilities that attackers exploit to generate DDoS attacks in IoT networks. Specifically, the proposed framework is tested using the CICDDoS2019 dataset to identify reflection attacks and exploitation attacks in TCP, UDP, and ICMP. The framework is also analyzed by varying network parameters such as the number of IoT attack nodes and payload to measure the performance of the SDN controller workload, CPU utilization, and attack detection time. The experimental results demonstrate that the proposed framework can efficiently detect and mitigate DDoS attacks while utilizing CPU resources effectively and in a shorter time compared to existing approaches. [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]

Software-Defined Networking (SDN) is one of the promising and effective approaches to establishing network virtualization by providing a central controller to monitor network bandwidth and transmission devices. This paper studies resource allocation in SDN by mapping virtual networks on the infrastructure network. Considering mapping as a way to distribute tasks through the network, proper mapping methodologies will directly influence the efficiency of infrastructure resource management. Our proposed method is called Effective Initial Mapping in SDN (EIMSDN), and it suggests writing a module in the controller to initialize mapping by arriving at a new request if a sufficient number of resources are available. This would prevent rewriting the rules on the switches when remapping is necessary for an n-time window. We have also considered optimizing resource allocation in network virtualization with dynamic infrastructure resources management. We have done it by writing a module in OpenFlow controller to initialize mapping when there are sufficient resources. EIMSDN is compared with SDN-nR, SSPSM, and SDN-VN in criteria such as acceptance rates, cost, average switches resource utilization, and average link resource utilization. [ABSTRACT FROM AUTHOR]

Fifth generation (5G) of the cellular network is envisioned to provide diverse services to the users including high data rates, energy efficiency, and low latency. The number of Internet of Things-connected devices, multimedia applications, and mobile users are expected to grow in the next few years. The massive growth in data consumption poses new challenges such as availability and scalability in future cellular networks. Software-defined networking (SDN) is a network technology that introduces flexibility and eases the management of network architecture by separating the control plane and data plane. In this paper, we survey the relevant works that propose an SDN-based mobile core architecture in order to move toward the 5G networks and to handle the complexity of an enormous number of connected devices to the network. We propose a taxonomy in which we categorize these works based on four factors: technology adoption, control plane implementation, evaluation technique, and architecture used in these works. We also provide the shortcomings of these architectures that should be tackled in future research works. [ABSTRACT FROM AUTHOR]

The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future Internet is supposed to face exponential growth in traffic, and highly complicated infrastructure, threatening to make conventional NTC approaches unreliable and even counterproductive. In recent days, AI Stimulated state-of-the-art breakthroughs with the ability to tackle extensive and multifarious challenges, and the network community is initiated by considering the NTC prototype from legacy rule-based towards a novel AI-based. Design and execution are applied to interdisciplinary become more essential. A smart home network supports various applications and smart devices within the proposed work, including e-health devices, regular computing devices, and home automation devices. Many devices accessible through the Internet by Home GateWay for Congestion (HGC) in a smart home. Throughout this paper, a Software-Defined Network Home GateWay for Congestion (SDNHGC) architecture for improved management of remote smart home networks and protection of the significant network's SDN controller. It enables effective network capacity regulation, focused on real-time traffic analysis and core network resource allocation. It cannot control the Network in dispersed smart homes. Our innovative SDNHGC expands power across the connectivity network, a smart home network enabling improved end-to-end monitoring of networks. The planned SDNHGC directly will gain centralized device identification by classifying traffic through a smart home network. Several of the current traffic classifications approach, checking deep packets, cannot have this real-time device knowledge for encrypted data to solve this issue. [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]

A continuing trend in many scientific disciplines is the growth in the volume of data collected by scientific instruments and the desire to rapidly and efficiently distribute this data to the scientific community. As both the data volume and number of subscribers grows, a reliable network multicast is a promising approach to alleviate the demand for the bandwidth needed to support efficient data distribution to multiple, geographically-distributed, research communities. In prior work, we identified the need for a reliable network multicast: scientists engaged in atmospheric research subscribing to meteorological file-streams. An application called Local Data Manager (LDM) is used to disseminate meteorological data to hundreds of subscribers. This paper presents a high-performance, reliable network multicast solution, Dynamic Reliable File-Stream Multicast Service (DRFSM), and describes a trial deployment comprising eight university campuses connected via Research-and-Education Networks (RENs) and Internet2 and a DRFSM-enabled LDM (LDM7). Using this deployment, we evaluated the DRFSM architecture, which uses network multicast with a reliable transport protocol, and leverages Layer-2 (L2) multipoint Virtual LAN (VLAN/MPLS). A performance monitoring system was developed to collect the real-time performance of LDM7. The measurements showed that our proof-of-concept prototype worked significantly better than the current production LDM (LDM6) in two ways. First, LDM7 distributes data faster than LDM6. With six subscribers and a 100 Mbps bandwidth limit setting, an almost 22-fold improvement in delivery time was observed with LDM7. Second, LDM7 significantly reduces the bandwidth requirement needed to deliver data to subscribers. LDM7 needed 90% less bandwidth than LDM6 to achieve a 20 Mbps average throughput across four subscribers. [ABSTRACT FROM AUTHOR]

Data routing processing in vehicular ad hoc networks (VANETs) with some QoS constraints, including delay bound and limited bandwidth, is an NP-Hard problem. Routing efficiency can be enhanced if more information is applied in routing decisions. Software-defined networks (SDNs) optimize network resources such as bandwidth by providing a global perspective of vehicular network topology, improving routing performance. This paper proposes a new method using the SDN integrated into vehicular networks. In this method, the controller nodes compute the r-least-cost node-disjoint path by having a global view of vehicles' information and trade-offs among the delay bound and path utility. The path utility factor is calculated based on the vehicles' residual bandwidth, packet congestion level, and link stability. In addition, according to the application type, packet size, requirement bandwidth, and delay bound of application, a priority-based scheduling algorithm is proposed. The proposed method calculates disjoint paths by considering the path utility factor and prioritizing the applications in data routing processing, which can react appropriately to increased traffic, continuous topological changes, and link interruptions in VANETs. The efficiency of the proposed method is superior against other methods in the literature in scenarios with different vehicle densities and various traffic loads in terms of data packet delivery fraction, average end-to-end delay, throughput, and normalized routing load through simulation. [ABSTRACT FROM AUTHOR]

We are witnessing a new era in autonomous systems with unprecedented user experiences, excessively recovered road safety and air quality, a wide range of transportation conditions and utilization cases, as well as a plethora of advanced time-sensitive applications. To realise this ambitious vision, Vehicular Ad-hoc Networks (VANET) and drones must have significantly improved communication. They must be remarkably intelligent and capable of simultaneously supporting hyper-fast, ultra-reliable, uninterrupted service, as well as low-latency massive information exchange. Although the Sixth Generation (6G) is successful in providing a seamless integration of heterogeneous elements as well as offering high coverage area with maximizing resource utilization; however, the accomplishment of faster computation, reducing bandwidth usage, low power consumption, and high throughput is remaining critical more than other purposes. This study introduces an End-To-End Oriented Management Architecture (EEOMA) based on fog computing and software-defined network (SDN) technologies for boosting the performance in hybrid networks of Vehicular Ad hoc Network (VANET) and Drone. Two techniques are proposed through EEOMA to manage the data transmission on both the SDN and the drones and meet network constraints: (i) a transport service aggregation approach for simplifying the amount of traffic data traveling across the network bandwidth and (ii) a normalized throughput of a channel with adjusting the interference ratio that indicates medium access control (MAC) layer overhead. The experimental results show that there is 1.2% to 6.1% enhancement of packet delivery and packet loss ratios. More trustworthiness in performance measurement is reached by the proposed EEOMA in terms of total throughput, response time, and power consumption. [ABSTRACT FROM AUTHOR]

Network Functions Virtualization (NFV) is a network architecture concept to improve network performance. This concept empowers the network capacities and reduces the cost and accelerates the network services and moving them to virtual severs. Mobility Management Entity (MME) is one of the feasible components for virtualization to control the plane due to its non-handling features for user data traffic. However, the MME does not handle the user data and not using any sort of hardware for data processing towards the cloud. The deployment of MME on the cloud is not an appropriate strategy due to the heavy load and complex processes. The main objective of this paper is to develop a suitable software deployment architecture Software-Defined Network-based Network Functions Virtualization (SDN-NFV) that can provide a cloud-optimized design for the MME. The proposed architecture is based on SDN networks to boost and enhance the traditional cellular network processes and functionalities. The proposed architecture contains three tiers including the data plane, control plane, and application plane. The data plane contains the switches, open flow protocol, and OpenSwitch virtual software and responsible to handle the data traffic and forward it to the correct destination based on flow entries in its flow table. The control plane is responsible for the entire network topology and contains the functionalities of traditional non-SDN routers and policies that come from the application plane. The application plane uses a virtualization environment where all the requirements are defined to the corresponding controller. The proposed architecture is evaluated in a simulation where it achieved better performance in terms of latency and throughput. [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]

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while providing a certain level of QoS for the users' flows and realizing the link load balancing. To reduce the network energy consumption, a minimum graph-based Ant Colony Optimization (ACO) approach is used in the first phase. It prunes and optimizes the network tree by turning unnecessary switches off and providing an energy-minimized sub-graph that is responsible for the network existing flows. In the second phase, an innovative weighted routing approach is developed that guarantees the QoS requirements of the incoming flows and routes them so that to balance the loads on the links. We validated our proposed approach by conducting extensive simulations on different traffic patterns and scenarios with different thresholds. The results indicate that the proposed routing method considerably minimizes the network energy consumption, especially for congested traffics with mice-type flows. It can provide effective link load balancing while satisfying the users' QoS requirements. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) emerges as one of the leading technologies to address the pressing networking problems such as network virtualization and data center complexity issues using programmable switches and controllers. To well exchange information and have coordinated control for the SDN environment, the multi-controller SDN network has been proposed. Unfortunately, this network architecture still faces the problem of faulty controllers that may forge the command arbitrarily, and then the whole network will be broken down easily. Thus, it is important to propose the fault-tolerance scheme under the SDN network system. In this paper, a new SDN environment called the Multi-Controller Overlay Groups (MCOG) where each device is managed by the controllers is proposed. Besides, the degree of the faulty influence will be revised to define the different bounds of fault tolerance in the MCOG environment. Furthermore, the new protocol named General Dynamic Multi-Controller Agreement (GDMCA) protocol is proposed to solve the Byzantine Agreement (BA) problem in the MCOG environment. Based on the proof of complexity, it can show that the proposed GDMCA protocol is optimal under the MCOG environment. [ABSTRACT FROM AUTHOR]

With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is proposed to solve the unbalanced network load, in which a seamless handover algorithm for wireless access points is used to calculate the access point with the highest weight, and a software-defined network controller controls the switching process. A joint allocation method of communication and computing resources based on deep reinforcement learning is proposed to minimize the terminal energy consumption and the system delay. A resource allocation model is based on minimizing terminal energy consumption, and system delay is built. The optimal value of task offloading decision and resource allocation vector are calculated with deep reinforcement learning. Experimental results show that the proposed method can reduce the network load and the task execution cost. [ABSTRACT FROM AUTHOR]

Software-Defined Networks (SDN) is a promising network technology that delivers the programable networks by separating control and data plane from the traditional architecture. The key issue in the SDN is to identify the number of controllers needed and its optimal location in the network. There are three major objectives of this paper. The primary objective is identifying the number of controllers needed. To identify it, a graph theory approach is adapted. The second objective is to identify optimal location of controllers in the network and what are all the nodes to be connected to the controller to enhance the performance of the network. For that, Pareto Integrated Tabu Search (PITS) algorithm is proposed. The final objective is to perform controller migration to handle dynamic situations like link failure and imbalance load of controller. A heuristic approach is proposed to perform migration. Real time topologies from Internet Topology Zoo are considered for experiment and the experimental results shows that the proposed methodology outperforms well in all situations considered for experiment. [ABSTRACT FROM AUTHOR]

In order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks. [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]

This paper proposes a scheme to reduce a size of a packet header for a segment routing (SR) scheme in a software-defined network (SDN). The SR scheme inserts a segment identification (SID) list into the packet header to indicate a path for the source–destination pair of the packet. The path can be split into different segments to suit the service requirement and the segments are carried by the SID-list whose length increases with the number of segments. This also increases the packet overhead, and an additional packet is needed if the packet length exceeds the maximum transmission unit (MTU). Moreover, it may not be possible to implement SR in SDN due to the limited number of stacked labels provided by the switch vendor. In the proposed scheme, the SID-list is replaced by a single tag to indicate a node edge, called a swapping node. The tag is replaced by a new tag at the swapping node. With this scheme, the size of SID-list is fixed and does not vary with the number of segments, and no additional packets are required. A mathematic model to balance the number of flow entries in each swapping node is introduced by minimizing the maximum number of flow entries in each swapping node over the network. We implement the proposed scheme on the transmission-Japan science information network (SINET5) and demonstrate confirms its functionality. [ABSTRACT FROM AUTHOR]

Computer networks covered the entire world and a serious and new development has not formed for many years. But companies and consumer organizations complain about the failure to add new features to their networks and according to their need, like much of the works to be done automatically and they also like to develop and expand their networks on the software side so they don't need new expensive hardware for many of the activities and needs of their network. Analysis of the control layers and writing data in Software-Defined Network (SDN) facilitate network management and accelerate innovation in network. In order to develop broad networks of SDN, often a large number controller is needed and located position of controllers in the SDN networks and can be raised as an important and basic issue in the field of SDN network and have impact on reliability of SDN networks. This paper focused on latency and reliability in SDN networks. The latency here means the delay in response to the request of data path that has a significant impact on network latency. In this paper it is shown that the number of controllers and their position can be effective on two measures; reliability and latency in SDN networks. [ABSTRACT FROM AUTHOR]

Space networking has captured increasing attentions because of its wide application scenarios. Facing to the technical challenges of space networking including topology alteration, non-realtime condition capture and control, and instable communication and control reliability, this article introduce software-defined networking (SDN) into space networking and proposes software-defined space networking, named SDSN. The architecture and the detailed strategy based routing algorithm are designed. SDSN has three key features: the predeterminate rules, strategy based routing algorithm, and redundant space-ground controlling strategy. These features address the three challenges pointedly. The simulation results confirm the advantages. [ABSTRACT FROM AUTHOR]

Packet classification in software-defined network has become more important with the rapid growth of Internet. Existing approaches focused on the data structure algorithms to classify the packets. But the existing algorithms lead to the problem of time budget and fails to accommodate large rule sets. Thus the key task is to design an algorithm for packet classification that inflicts process overhead, and the algorithm should handle large databases of classification rule. These challenging issues are achieved by proposing rectified linear unit deep neural network. The aim of this work is twofold. First various hyper-parameter values are analyzed in order to examine how they affect the packet classification performance of deep neural network; and their performance is compared with that of popular methods, e.g., K-nearest neighbor and support vector machines. The open-source TensorFlow deep learning framework with the support of NVidia GPU units is used to carry out this work, thus allowing a large number of rules to predict the exact flow. The result shows that the proposed method performs well, and hence, this model is more suitable for large classification rules. [ABSTRACT FROM AUTHOR]

The security threats to software-defined networks (SDNs) have become a significant problem, generally because of the open framework of SDNs. Among all the threats, distributed denial-of-service (DDoS) attacks can have a devastating impact on the network. We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model. The feature-pattern graph model presented employs network patterns as nodes and similarity as weighted links; it can demonstrate not only the traffic header information but also the relationships among all the network patterns. The similarity between nodes is modeled by metric learning and the Mahalanobis distance. The proposed method can discover DDoS attacks using a graph-based neighborhood classification method; it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph model via local or global updates. Experiments on two datasets prove the feasibility of the proposed method for attack behavior discovery and graph update tasks, and demonstrate that the graph-based method to discover DDoS attack behaviors substantially outperforms the methods compared herein. [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]

In order to use the energy more efficiently, network algorithms and protocols must incorporate in their decision mechanisms some functions focused on saving energy. In this paper we have studied different features and parameters of wireless networks to establish their relationship with the power consumption. First, we have analyzed the variation of power consumption of access points (APs) in function of the antenna transmission power. Second, we have collected user information from real public wireless network to determine the real requirements of network resources in real time basis. Based on this information, a new extension for Openflow protocol over Software-Defined Networking (SDN) networks will be proposed to manage the wireless network, with the aim to keep the optimal network performance with minimal power consumption. This extension introduces new Openflow messages and a new function to be incorporated in SDN controllers and Openflow enabled devices. The proposal will be validated by appropriated simulations based on real scenarios: a shopping center and a municipal wireless network. [ABSTRACT FROM AUTHOR]

Software-defined network (SDN) is becoming the most suitable solution to cover huge network development cost without concerning about their physical limitations. Applications working with SDN for data traffic control are familiar with their utilization in the system, but semantics are not part of the data model for reasoning to play a role. Furthermore, it clarifies the idea that current data models are not sufficient to cover recent trends toward data traffic monitoring requirements. On the other hand, Resource Description Framework (RDF) data model for Semantic Web (SW) has become a standard for data modeling and analysis. This data model has wider support for machine intelligence. A methodology becomes desirable so that the data can be used to track updates intact intelligently with the help of data information transformation. Such method can prove to be beneficial for systems forming broad and real-time distribution using SDN platform for networking. Therefore, such an approach can reduce the conceptual gap between intelligence and data monitoring for better throughput. This research attempts to provide a methodology for covering data mapping, data transformation, and change control for traffic control. It is resulting in forming a cooperative environment for SDN-based adaptation between system and application, which brings data traffic monitoring at entirely different scales. [ABSTRACT FROM AUTHOR]

Although the perimeter security model works well enough when all internal hosts are credible, it is becoming increasingly difficult to enforce as companies adopt mobile and cloud technologies, i.e., the rise of bring your own device (BYOD). It is observed that advanced targeted cyber-attacks usually follow a cyber kill chain; for instance, advanced targeted attacks often rely on network scanning techniques to gather information about potential targets. In response to this attack method, we propose a novel approach, i.e., an "isolating and dynamic" cyber defense, which cuts these potential chains to reduce the cumulative availability of the gathered information. First, we build a zero-trust network environment through network isolation, and then multiple network properties are maneuvered so that the host characteristics and locations needed to identify vulnerabilities cannot be located. Second, we propose a software-defined proactive cyber defense solution (SPD) for enterprise networks and design a general framework to strategically maneuver the IP address, network port, domain name, and path, while limiting the performance impact on the benign network user. Third, we implement our SPD proof-of-concept system over a software-defined network controller (OpenDaylight). Finally, we build an experimental platform to verify the system's ability to prevent scanning, eavesdropping, and denial-of-service attacks. The results suggest that our system can significantly reduce the availability of network reconnaissance scan information, block network eavesdropping, and sharply increase the cost of cyber-attacks. [ABSTRACT FROM AUTHOR]

Abstract—: The correctness and efficiency of the homeostatic approach to ensuring cyber-sustainability based on the software-defined networking (SDN) technology is proven. The cyberphysical system Smart Home is simulated using this technology. The authors conducted a series of experiments in which the system was countering various attacks. The software-defined network used three self-adaptation mechanisms to ensure cyber-sustainability in the experiment. The experiments confirmed that the SDN-based Smart Home system retained its sustainability under destructive impacts. [ABSTRACT FROM AUTHOR]

Abstract: An approach is presented to the construction of a hierarchical security management system for large-scale dynamic communication networks (MANET, VANET, FANET, WSN, etc.) with the use of software-defined network technologies and supercomputer elastic computations. The results of experiments to evaluate the effectiveness of the proposed approach are presented. [ABSTRACT FROM AUTHOR]

Virtual cluster, as a fundamental service of cloud computing, is an important delivery model of cloud services. Allocating physical resources for a virtual cluster is known as virtual cluster embedding (VCE), which has a significant impact on the performance. VCE includes the components of virtual machine, virtual switch, virtual link, virtual software-defined network (SDN) controller and cluster system controller. VCE needs to be considered comprehensively to adapt the influencing factor weight proportion and to be flexible for different types of embedding. This paper, based on the topology of the cloud data center network, develops a coordinated VCE approach, called CoVCE. The approach combines virtual network with SDN to form virtual SDN and weakens slightly the constraints of VM placement. The network centrality, correlation property and resource fragmentation are optimized with multiple objectives to receive more requests, increase the throughput and decrease network delay and runtime. The CoVCE method integrates not only the logical topology of virtual machines in the cluster but also the relationship with virtual switch, virtual link and control services. This does not only optimize the placement location of virtual components, but also allows the virtual resources to migrate upon virtual cluster requests. The method further improves the utilization of physical resources and reduces resource fragmentation. According to extensive simulation and emulation experiments and comparison with correlative algorithms, CoVCE effectively reduces network delays, offers a higher embedding efficiency, improves user experiences and, to some extent, also improves the revenue/cost ratio and throughput. [ABSTRACT FROM AUTHOR]

The article studies the approach to building secured vehicle networks (VANET, FANET, MARINET, etc.) using the software-defined network technology. The experimental results on evaluating the effectiveness of architectures of secured traffic networks created using supercomputer modeling have been presented. [ABSTRACT FROM AUTHOR]

Currently, different kinds of security devices are deployed in the cloud datacenter environment and tenants may choose their desired security services such as firewall and IDS (intrusion detection system). At the same time, tenants in cloud computing datacenters are dynamic and have different requirements. Therefore, security device deployment in cloud datacenters is very complex and may lead to inefficient resource utilization. In this paper, we study this problem in a software-defined network (SDN) based multi-tenant cloud datacenter environment. We propose a load-adaptive traffic steering and packet forwarding scheme called LTSS to solve the problem. Our scheme combines SDN controller with TagOper plug-in to determine the traffic paths with the minimum load for tenants and allows tenants to get their desired security services in SDN-based datacenter networks. We also build a prototype system for LTSS to verify its functionality and evaluate performance of our design. [ABSTRACT FROM AUTHOR]

Moving target defense is a revolutionary technology changing the antagonistic pattern between attack and defense, with end-point information hopping one of the hotspots in this field. In order to counterpoise the defensive benefit of end-point information hopping and service quality of network system, a novel technique named self-adaptive end-point hopping technique based on adversary strategy awareness is proposed. To solve the blindness problem of hopping mechanism in the course of defense, hopping triggering based on adversary strategy awareness is applied to guide the choice of hopping mode by discriminating the scanning attack strategy, which enhances targeted defense. Furthermore, aimed at the low availability problem caused by limited network resource and high hopping overhead, satisfiability modulo theories are used to formally describe hopping constraints, so as to ensure low hopping overhead. Finally, both theoretical and experimental analyses are performed, demonstrating that the proposed technique can ensure low hopping overhead, while effectively discriminating and defending different types of scanning attacks. [ABSTRACT FROM AUTHOR]

Ubiquitous data center optical interconnection between user and data center has become a promising scenario gradually to accommodate high-performance services with end-to-end quality guaranteed. In our previous work, we implemented to provision data center application in inter-data center optical interconnection. In view of this, this study extends to consider the networking relationship between user and data center. We propose a novel software-defined clustered-optical access networking (SD-COAN) architecture for data center services in ubiquitous data center optical interconnection. A cross-stratum flow schedule (CSFS) strategy is introduced for SD-COAN based on the proposed architecture. The SD-COAN can enhance the responsiveness to the dynamic end-to-end data center demands and globally optimize optical access network and application resources effectively. The overall feasibility and efficiency of the proposed architecture are experimentally verified on our OpenFlow-based enhanced SDN testbed. The performance of CSFS strategy under heavy traffic load scenario is also quantitatively evaluated based on SD-COAN architecture in terms of resource occupation rate, average throughput and average provisioning delay, compared with other provisioning strategy. [ABSTRACT FROM AUTHOR]

Traditional network architecture is inflexible and complex. This observation has led to a paradigm shift toward software-defined networks (SDNs), in which the network control level is separated from the data link layer. This change became possible because of the control plane transfer from switching equipment to software modules that run on a dedicated server called a controller (or a network operating system) or to network applications that work with this controller. Methods of presentation, storage, and communication interfaces with network topology elements available to SDN controller users are the most important aspects of network operating systems because the operation of some key controller modules depends heavily on the internal representation of the network topology. Firewall and routing modules can be cited as examples of these modules. This paper considers the methods used to represent and store the network topology, as well as communication interfaces with corresponding modules of the Floodlight controller. An alternative algorithm for exchanging messages on the changes in the network topology between the controller and network applications has been proposed and developed. The algorithm makes it possible to issue notifications based on a subscription to relevant events. An API for the module of interacting with applied programs of the SDN controller has been developed. The Topology Tracker module has been designed based on this algorithm and API. In active mode, this module can inform network applications about the changes in the network topology and store its compact representation for the interaction acceleration. [ABSTRACT FROM AUTHOR]

Passive optical network (PON) has attracted much attention by the emergence of high-bitrate video-centric applications over the Internet in the access side. In the current commercial PON, the configuration of optical line termination equipment is needed to modify through network management system manually and periodically in the local areas. The traffic flow of different users lacks dynamic control and intelligent schedule. In this paper, we propose and implement a novel software-defined optical access network (SDOAN) architecture for remote unified control based on OpenFlow-enabled PON. Based on the proposed architecture, a service-aware flow scheduling strategy is introduced to flexibly and efficiently allocate the network bandwidth resources and detect the status of network flows in real time. The SDOAN can enhance the resource utilization and QoS guarantee of each user effectively through unified control manner, and reduce the operating expense by remote interaction and operation. We have designed and verified experimentally SDOAN on our test bed with OpenFlow-enabled OLTs. The overall feasibility and efficiency of the proposed architecture are also experimentally demonstrated and compared with interleaved polling with adaptive cycle time strategy in terms of resource occupation rate and average delay. [ABSTRACT FROM AUTHOR]

The optical access networks and aggregation networks are necessary to be controlled together to improve the bandwidth resource availability globally. Unified control architecture for optical access networks and aggregation networks is designed based on software-defined networking controller, the function modules of which have been described and the related extended protocol solution has been given. A software-defined dynamic bandwidth optimization (SD-DBO) algorithm is first proposed for optical access and aggregation networks, which can support unified optimizations and efficient scheduling by allocating bandwidth resources from a global network view in real time. The performance of the proposed algorithm has been verified and compared with traditional DBA algorithm in terms of resource utilization rate and average delay time. Simulation result shows that SD-DBO algorithm performs better. [ABSTRACT FROM AUTHOR]

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Due to the wide spread of 5G networks, network users have higher requirements for communication delays. Software-defined network is a new paradigm of decoupling the control logic from packet forwarding devices. We can reduce communication latency in the network by optimizing the location of the controller and improve the communication performance of the network. In this paper, the controller deployment problem of multi-network area is studied in order to reduce the average communication latency. In order to solve this problem, we proposed an optimized DPC algorithm. Specifically, on the basis of DPC algorithm we quoted the idea of triangular stability from the BeeDPC algorithm, and introduced SC measurement indicators, the degree of separation between sub-network regions and the degree of aggregation within the sub-network region, so as to divide the network region more reasonably. At the same time, we have break out of the constraints in the DPC algorithm and introduced closeness centrality to get a more reasonable placement scheme and reduced the limitations of the DPC algorithm. Simulation results have shown that the optimized DPC algorithm can effectively reduce the average delay between the control layer and the data layer, improve the network performance, and enhance network stability and reliability. [ABSTRACT FROM AUTHOR]