Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> In today's IT systems, cyber security requires fine-grained, flexible, adaptable and cost optimized monitoring mechanisms. The emergence of new networking technologies, like Network Function Virtualization (NFV) and Software Defined Networking (SDN), opens up new venues for large scale adoption of these cyber security tools. In particular, Deep Packet Inspection (DPI) engines can be virtualized and dynamically deployed as pieces of software on commodity hardware. Deploying such software DPI engines is costly in terms of license fees and power consumption. Designing cost effective DPI engine deployment strategies that meet the cybersecurity operational constraints is thus mandatory for the adoption of this approach. For this purpose, we propose a method, based on genetic algorithms, that optimizes the cost of DPI engine deployment, minimizing their number, the global network load and the number of unanalyzed flows. We conduct several experiments with different types of traffic and different cost structures. The results show that the method is able to reach a trade-off between the number of DPI engines and network load. Furthermore, the global cost can be reduced up to 58% when relaxing the constraint on the used link capacity, that is the provisioning rate. <s> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Load balancing service is essential for distributing workload across server farms or data centers and mainly provided by dedicated hardware. In recent years, the concept of Software-Defined Networking (SDN) has been applied successfully in the real network environment, especially by OpenFlow designs. This paper presents an OpenFlow-based load balancing system with the genetic algorithm. This system can distribute large data from clients to different servers more efficiently according to load balancing policies. Furthermore, with the pre-configured flow table entries, each flow can be directed in advance. Once the traffic burst or server loading increased suddenly, the proposed genetic algorithm can help balance workload of server farms. The experiments demonstrate the better performance of the proposed method compared to other approaches. <s> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> The Software-Defined Network (SDN) approach decouples control and forwarding planes. Such separation introduces reliability design issues of the SDN control network, since disconnection between the control and forwarding planes may lead to severe packet loss and performance degradation. This paper addresses the problem of placing controllers in SDNs, so as to maximize the reliability of control networks. After presenting a metric to characterize the reliability of SDN control networks, several placement algorithms are developed. We evaluate these algorithms and further quantify the impact of controller number on the reliability of control networks using real topologies. Our approach can significantly improve the reliability of SDN control networks without introducing unacceptable latencies. <s> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. A traditional SDN implementation relies on a logically centralized controller that runs the control plane. However, in a large-scale WAN deployment, this rudimentary centralized approach has several limitations related to performance and scalability. To address these issues, recent proposals have advocated deploying multiple controllers that work cooperatively to control a network. Nonetheless, this approach drags in an interesting problem, which we call the Dynamic Controller Provisioning Problem (DCPP). DCPP dynamically adapts the number of controllers and their locations with changing network conditions, in order to minimize flow setup time and communication overhead. In this paper, we propose a framework for deploying multiple controllers within an WAN. Our framework dynamically adjusts the number of active controllers and delegates each controller with a subset of Openflow switches according to network dynamics while ensuring minimal flow setup time and communication overhead. To this end, we formulate the optimal controller provisioning problem as an Integer Linear Program (ILP) and propose two heuristics to solve it. Simulation results show that our solution minimizes flow setup time while incurring very low communication overhead. <s> BIB004 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> We present design, implementation, and an evaluation of an ant colony optimization (ACO) approach to flow routing in software-defined networking (SDN) environments. While exploiting a global network view and configuration flexibility provided by SDN, the approach also utilizes quality of experience (QoE) estimation models and seeks to maximize the user QoE for multimedia services. As network metrics (e.g., packet loss) influence QoE for such services differently, based on the service type and its integral media flows, the goal of our ACO-based heuristic algorithm is to calculate QoE-aware paths that conform to traffic demands and network limitations. A Java implementation of the algorithm is integrated into SDN controller OpenDaylight so as to program the path selections. The evaluation results indicate promising QoE improvements of our approach over shortest path routing, as well as low running time. <s> BIB005 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> This paper introduces A4SDN, a distributed, adaptive, load-balancing algorithm for traffic engineering on Software-Defined Networks. A4SDN, i.e. the Adaptive Alienated Ant Algorithm for Software-Defined Networking, is based on the Alienated Ant Algorithm (AAA), a stochastic-based, heuristic approach used to solve combinatorial and multi-constraint optimisation problems. Based on a non natural ants' behaviour, the AAA forces the ants in searching for food to distribute themselves over all the available paths rather than converge to a single one. This strategy, applied to the packets to route, supplies an autonomic dynamic routing and leads to a better exploitation of the network bandwidth, enforcing best effort traffic and improving network performance. A comparison between A4SDN with two Dijkstra-based routing solutions clearly shows that A4SDN is able to guarantee a higher throughput associated with a lower delay and packet loss rate. <s> BIB006 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the hard constraints of network measurement resources. This paper proposes a new framework, SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can leads to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications including per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring 8.8\% of per-flow path delays in Harvard network, congested paths can be detected with probability 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet. <s> BIB007 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> A Computer network or data communication network is a telecommunication network that allows computers to exchange data. Computer networks are typically built from a large number of network devices such as routers, switches and numerous types of middle boxes with many complex protocols implemented on them. They need to accomplish very complex tasks with access to very limited tools. As a result, network management and performance tuning is quite challenging. Software-Defined Networking (SDN) is an emerging architecture purporting to be adaptable, cost-effective, dynamic and manageable pursuing to be suitable for the high-bandwidth, changing nature of today's applications. SDN architectures decouples network control and forwarding functions, making network control to become directly programmable and the underlying infrastructure to be abstracted from applications and network services. The network security is a prominent feature of the network ensuring accountability, confidentiality, integrity, and protection against many external and internal threats. An Intrusion Detection System (IDS) is a type of security software designed to automatically alert administrators when someone or something is trying to compromise information system through malicious activities or through security policy violations. Security violation in SDN environment needs to be identified to prevent the system from an attack. The proposed work aims to detect the attacks on SDN environment. Detecting anomalies on SDN environment will be more manageable and efficient. <s> BIB008 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Software Defined Network (SDN) has been envisioned as the next generation network infrastructure, which simplify network management by decoupling the control plane and data plane. It is becoming the leading technology behind many traffic engineering solutions, since it allows a central controller to globally plan the paths of the flows. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we address the routing rule space occupation problem for multiple unicastSessions with Quality-of-Service (QoS) constraints. To our best knowledge, this is the first work to joint routing rule optimization with traffic engineering for multipath flows. We formulate the problem using Mixed Integer Linear Programing (MILP) and propose an approach based on ant colony algorithm to solve it. Finally, we evaluate the expected performance of our proposed algorithm through a simulation study. <s> BIB009 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Software Defined Networking is a novel network architecture, which separates data and control plane by OpenFlow. The feature of centralized control to acquire and allocate of global network resource. So, the link load-balancing of SDN is not as difficult as the traditional network. This paper proposes a link load-balancing algorithm based on Ant Colony Optimization(LLBACO). The algorithm uses the search rule of ACO and takes link load, delay and pack-loss as impact factors that ants select next node. For maintaining link load-balancing and reducing end-to-end transmission delay, the widest and shortest path in the all paths can be gained by ants. The results of simulations show that LLBACO can balance the link load of network effectively, improve the Quality of Service(QoS) and decrease network overhead, compared with existing algorithm. <s> BIB010 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Software-Defined Network (SDN) allows a centralized controller to globally plan packets forwarding according to the operator's objectives. It may require more forwarding rules, and multi-path routing which is allowed in SDN to improve network utilization and throughput means more forwarding rules. However, the forwarding table in TCAM-based SDN switch is an important but limited resource. With this limitation, to satisfy global network objective, such as maximum flow, we formulate this problem as the Bounded Forwarding-Rules Maximum Flow (BFR-MF) problem which is NP-hard. We maintain fairness among flows to guarantee the performance of individual flows and a certain level of QoS. We improve the Ant Colony Optimization algorithm, such as adding heuristic information based on forwarding table size, and propose the WiseAnt Colony Optimization (WACO) algorithm to solve the BFR-MF problem. We use normalized maximum flow to evaluate the performance of WACO algorithm. Extensive simulations show that WACO algorithm performs better in network utilization on the premise of a certain level of QoS, especially for the data center network. <s> BIB011 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Software Defined Nerwork (SDN) provides centralized and opened way to manage the network by separating both control plane and data plane each other. The controller defined as a software is responsible for a control plane. If there is too much load on a specific controller, controller's suboptimal performance problem will be occurred. Because of this problem, network will provide services in an inefficient way. OpenFlow provides a way to connect switches with multiple controllers so that the controllers' load can be balanced. This paper suggests a solution to distribute and balance the load on controllers based on genetic algorithm when imbalance is detected. Then we can get an optimized way to deploy the switches to controller and network services can be provided efficiently. <s> BIB012 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> In applications such as video surveillance systems, cameras transmit video data streams through network in which quality of received video should be assured. Traditional IP based networks cannot guarantee the required Quality of Service QoS for such applications. Nowadays, Software Defined Network SDN is a popular technology, which assists network management using computer programs. In this paper, a new SDN-based video surveillance system infrastructure is proposed to apply desire traffic engineering for practical video surveillance applications. To keep the quality of received videos adaptively, usually Constraint Shortest Path CSP problem is used which is a NP-complete problem. Hence, heuristic algorithms are suitable candidate for solving such problem. This paper models streaming video data on a surveillance system as a CSP problem, and proposes an artificial bee colony ABC algorithm to find optimal solution to manage the network adaptively and guarantee the required QoS. The simulation results show the effectiveness of the proposed method in terms of QoS metrics. <s> BIB013 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> The vast majority of Web services and sites are hosted in various kinds of cloud services, and ordering some level of quality of service (QoS) in such systems requires effective load-balancing policies that choose among multiple clouds. Recently, software-defined networking (SDN) is one of the most promising solutions for load balancing in cloud data center. SDN is characterized by its two distinguished features, including decoupling the control plane from the data plane and providing programmability for network application development. By using these technologies, SDN and cloud computing can improve cloud reliability, manageability, scalability and controllability. SDN-based cloud is a new type cloud in which SDN technology is used to acquire control on network infrastructure and to provide networking-as-a-service (NaaS) in cloud computing environments. In this paper, we introduce an SDN-enhanced Inter cloud Manager (S-ICM) that allocates network flows in the cloud environment. S-ICM consists of two main parts, monitoring and decision making. For monitoring, S-ICM uses SDN control message that observes and collects data, and decision-making is based on the measured network delay of packets. Measurements are used to compare S-ICM with a round robin (RR) allocation of jobs between clouds which spreads the workload equitably, and with a honeybee foraging algorithm (HFA). We see that S-ICM is better at avoiding system saturation than HFA and RR under heavy load formula using RR job scheduler. Measurements are also used to evaluate whether a simple queueing formula can be used to predict system performance for several clouds being operated under an RR scheduling policy, and show the validity of the theoretical approximation. <s> BIB014 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness. <s> BIB015 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Summary Introduction In the past decades, telemedicine has attracted much attention due to its usefulness for delivering medical services from remote location. One of the most important parts of telemedicine is telesurgery, in which a surgeon performs surgery on the patient from remote location by using telecommunication networks. Due to the surgeon needs to watch the patient side transmitted video with high quality; it is necessary to provide fast and suitable communication path between the surgeon and patient. Emerging Software Defined Network (SDN) as a new network paradigm facilitated Quality of Service (QoS) provisioning in many applications. Decoupling data plane from control plane in SDN, helps to manage based on end users requirements. This paper proposes a new heuristic traffic engineering method for telesurgery application using Ant Colony Optimization (ACO) algorithm. Patients and methods This paper models QoS provisioning in Telesurgery as a Constraint Shortest Path (CSP) linear programming problem. Moreover, due to NP-completeness of CSP problem, this paper proposes a solver method using ACO algorithm. To the best of our knowledge, this is the first paper which investigates QoS provisioning for telesurgery in SDN. Results Results of performance evaluation showed that the proposed method is better than baseline method. The proposed method improves all three important QoS parameters, average End-to-End delay, packet loss ratio and PSNR with values of 56.3%, 50.5% and 1.33, respectively. Discussion providing QoS in telesurgery applications is one of the most challenging issues. By leveraging the capability of SDN, we can develop appropriate mechanisms to guarantee QoS requirements in critical applications such as Telesurgery. Conclusion Using SDN together with optimization models such as CSP for providing QoS in Telesurgery can improve the quality of the received video in an operation room. This mechanism causes increment in the performance of Telesurgery. <s> BIB016 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Energy consumption is a key concern in the deployment and operation of current data networks, for which software-defined networks (SDNs) have become a promising alternative. Although several works have been proposed to improve the energy efficiency, these techniques may lead to performance degradations when QoS requirements are neglected. Inspired by this problem, this letter introduces a new routing strategy, jointly considering QoS requirements and energy awareness in SDN with in-band control traffic. To that end, we present a complete formulation of the optimization problem and implement a multi-objective evolutionary algorithm. Simulation results validate the performance improvement on critical network parameters. <s> BIB017 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Moving Target Defense (MTD) is a paradigm in cyber-security that proposes cyber-systems that continually change their configurations to avoid easy surveillance from adversaries. Despite its promise, MTDs have yet to be fully adopted in real world systems. Using the concepts from the general theory of MTD, this paper proposes that the goal of MTDs i.e continually adapting to their environment to evade attack is similar to the concept of an organism evolving to survive in its habitat. This implies that Evolutionary Computation (EC) techniques could be used to fulfill the goal of MTD, specifically in relation to intrusion detection in the emerging field of Software Defined Networks (SDNs). The programmable nature of SDNs provide a degree of flexibility not possible in conventional networks. Thus, making it possible to completely automate the configuration of such networks. <s> BIB018 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Software Defined Networking (SDN) technologies provide applications opportunities to manipulate underlying network flows and topologies via network controllers during runtime. In cloud environments, networked virtual machines can be enhanced by SDN by providing applications with controllable infrastructures to meet system-level quality requirements; however, customizing a suitable network topology with optimally placed controller(s) for given quality requirements and workload characteristics is often not an easy task. We call such problem virtual SDN network planning problem. In this paper, a Topology-Controller planner (TCPlanner) is proposed for customizing the network topology and placing the controllers. Experiments with different scales of network show that our approach can effectively plan virtual SDN networks to meet the various QoS requirements and reduce costs. <s> BIB019 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> To support emerging traffic demands, metro optical networks evolve over time with the addition of new links to relieve network bottlenecks and congestion. These new resources need to be optimally located in the network, in order to minimize the cost of the new deployment. Spectrum-fragmentation and network-wide load imbalance impede the optimal allocation of network resources. With the recent convergence of advanced flexible and programmable optical devices with emerging software-defined network paradigms, it is possible to flexibly and dynamically defragment the spectrum and balance the load on the network. This study introduces an optimization strategy for software-defined elastic optical networks for fiber-load balancing across the network, while minimizing the cost of the resulting service disruption. An entropy-based metric is proposed for measuring load imbalance and used to design utility functions for the joint optimization, taking into consideration the optical and defragmentation constraints. <s> BIB020 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Abstract A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today’s large-scale networks, this is a challenging task due to the scarcity of network measurement resources and the hard constraints that this imposes. This paper proposes a new framework, called SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can lead to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications for per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring only 8.8% of all per-flow path delays in Harvard network [1], congested paths can be detected with probability of 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet. <s> BIB021 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Elastic optical networks (EONs) are considered as the most promising technology for interconnecting data centers. With the rapid growth of inter- datacenter traffic, power consumption of EONs becomes a significant challenge. In this work, we present a lightpath management algorithm that uses traffic prediction techniques to eliminate unnecessary lightpath termination and re- establishment so as to decrease switching power and enhance the energy efficiency of the network. Our algorithm builds upon the centralized control and capabilities of software defined networking (SDN) technology. Numerical results show that the proposed algorithm is effective in achieving substantial savings in power consumption while maintaining a bandwidth blocking ratio at levels comparable to those of earlier algorithms. <s> BIB022 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> The 5th generation technologies, i.e. 5G based on a new flexible architecture named by Software Defined Network(SDN) technologies, which is adaptable to use cases and scenarios. SDN allows more flexible and dynamic network resource management that is becoming a key problem in 5G. To meet the needs of all kinds of traffics, the efficient resource allocation algorithm has been proposed which is of extreme importance for SDN. In this paper, the merit of quick converging to the global optimal solution was used to design the fitness function of the network resource management. The ability of fast random search of particle swarm optimization was used to realize the update and optimization. The simulation results show that can reduce the energy consumption of the network and improve the utilization rate of cyber source at the same time. <s> BIB023 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> With the development of cloud computing, the current Software Defined Networking (SDN) data center which is based on the Open Source Cloud platform OpenStack cannot satisfy multiple users' service requirements. OpenStack cannot achieve centralized control of the physical network in cloud data center. In order to satisfy the user's flexible control of the data center network, a multi-tenant virtual network customization in cloud data center is proposed in this paper. SDN with the advantages of centralized control and programming is integrated to OpenStack cloud data center. Isolated tenant virtual SDN networks (vSDN) are created in the cloud data center via the virtual network mapping model based on particle swarm optimization (PSO) algorithm. The vSDN network is controlled by tenant's own controller. Virtual network mapping performance, link customization and data transmission performance are tested in this paper. Results show that the virtual network mapping model based on PSO algorithm compared to shortest path (SP) algorithm, greatly improve the utilization rate of the underlying network bandwidth. At the same time, the SDN mode does not reduce the performance of data transmission on OpenStack cloud platform. The virtual network link customization improves the efficiency of data transmission for tenant. <s> BIB024 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Software-defined networking (SDN) introduces a new method in networking that by offering programmability and centralization, it can dynamically control and configure networks. In traditional networks, data plane did the whole forwarding process, but SDN decouples data plane and control plane by using programmable software controllers for deciding how to forward different flows. By implementing control plane in a software-based independent layer, the network management will become much easier and new policies can be applied to the network by changing a few lines of code. Since the resource allocation and meeting the required service-level agreement are really important in large-scale networks such as cloud datacenters, using SDN can be very useful. In these networks, one logically centralized controller cannot handle the whole network traffic and it will become network bottleneck. Therefore, multiple distributed controllers should be allocated in different regions of the network. Since the request rate of switches varies in time, by dynamic allocation of controllers, network resources will be allocated efficiently and this approach can also reduce power consumption. In this paper, we are going to propose a framework for provisioning software controllers in cloud datacenters by using metaheuristic algorithms. These algorithms can be less accurate compared to other kinds, but their main characteristics like simplicity, flexibility, derivation free, and local optimum avoidance make them a good nominee for solving controller provisioning problem and controller placement problem. Our framework improves computation time and reaches better results compared to other allocation techniques, but it is less accurate in some scenarios. Therefore, we believe metaheuristic approach can be very useful in developing new technologies for SDN in the future. <s> BIB025 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> The recent advent of Software Defined Networking (SDN) and intelligent networks have prompted the researcher to conduct further investigations into the high-density Wireless Sensor Network (WSN). WSNs have inherent issues that limit their performance, such as sensor resource restrictions that affect power supply, memory, processing units and communications capabilities. This paper proposes new clustering, using a Whale Optimisation Algorithm (WOA) based on the concept of SDN. The proposed protocol considers both sensor resource restrictions and the random diversification of node density in the geographical area. It begins by dividing the sensing area by the SDN controller into virtual zones (VZs) to balance the number of cluster heads (CHs) according to the node density in each VZ; it then uses the WOA, which considers residual energy, communication cost and node density, to define the optimal set of CHs. Simulation results show that the proposed protocol has improved network lifetime and resulted in efficient energy dissipation; furthermore, it has increased the number of packets sent to the sink by approximately 55% compared to traditional protocols, and by about 20% compared to an optimisation based routing protocol. <s> BIB026 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Abstract There has been a significant increase in mobile data traffic in recent years and by the advent of the IoT (Internet of Things), it will continue to rise. Since the current wireless systems cannot handle this amount of traffic, the next generation of the mobile standard is planning to overcome the obstacles by using new technologies such as Cloud Computing, Software Defined Networking (SDN), and Network Function Virtualization (NFV). Cloud Computing will provide sufficient resources, required for the mobile networks, to handle the ever-increasing amount of traffic. Additionally, using SDN as well as NFV in cloud datacenters can lead to better manageability in the networks and easier development of network applications in the future. Since cloud datacenters as well as mobile networks are inherently large-scale networks, a single SDN controller (traditional SDN) cannot handle decision-making processes of these networks and control plane must be distributed between several software SDN controllers. Therefore, allocating SDN controllers efficiently in these networks becomes important, which can lead to lesser energy consumption, lesser CAPEX, and lesser OPEX. In addition, the highly variable pattern of these networks makes it necessary to allocate controllers dynamically. However, by the growth of the networks, dynamic allocation of controllers can turn into an NP-Hard problem and using metaheuristic algorithms instead of deterministic ones to solve this problem can show better results as well as better computation time. In this study, we proposed a metaheuristic-based framework for dynamic controller allocation for the 5th generation of mobile technology (5G). We simulated our framework in MATLAB, compared our framework with static allocation technique, and compared our algorithm with another metaheuristic algorithm—PSO. Simulation results show that our algorithm ameliorates computation time and the computed solutions for different Quality of Services (QoS) are feasible, acceptable, and accurate. <s> BIB027 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: 1) the elapsing of a predefined time interval, or, 2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3x compared to ECMP while packet loss is decreased up to 2x. <s> BIB028 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> A software-defined network (SDN) is a technology that supports computer network administrators. However, the centralized control plane architecture of SDNs makes them vulnerable to harmful security threats. In this paper, we propose a secure cloud (SecSDN-cloud) architecture that includes user authentication, routing, attack resistance, and third-party monitoring. The goal of this paper is to design an SDN-cloud environment with integrated security that can resist three different attack types: flow table overloading, control plane saturation, and Byzantine attacks. A novel digital signature with chaotic secure hashing is used for user authentication, followed by an enhanced particle swarm optimization multi-class routing protocol to improve the quality of service. Controllers are assigned to switches by integrating an enhanced genetic algorithm with a modified cuckoo search algorithm. The malicious flow identification includes the analysis of five-tuples constructed from features extracted from packets. We implemented the proposed SecSDN-cloud in the OMNeT++ simulator and evaluated its performance in terms of packet loss, end-to-end delay, throughput, latency, and bandwidth. <s> BIB029 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> The architecture of SDN helps in early detection and mitigation of DDoS attacks, which is a challenge in the traditional network. With appropriate identification patterns and detection scheme, the SDN controller detects DDoS attacks in infant stages and suppresses their impact on the entire network. This paper proposes a feature set for classification of DDoS attacks from normal traffic. Further, a system model for detection of DDoS attacks implemented with SDN controller is proposed. The detection module in system model is based on RBF network having PSO optimized learning that is further compared with several schemes. The effectiveness of the system model has been verified by its real-time emulation with mininet emulator. The proposed system model is efficient in classifying heavy traffic load in the network from that of DDoS attacks and is capable of handling the DDoS attacks in their early stages itself. <s> BIB030 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Abstract Software Defined Networks (SDN) is a popular modern network technology that decouples the control logic from the underlying hardware devices. The control logic has implemented as a software entity that resides in a server called controller. In a Software-Defined Wide Area Network (SDWAN) with n nodes; deploying k number of controllers ( k n ) is one of the challenging issue. Due to some internal or external factors, when the primary path between switch to controller fails, it severely interrupt the networks’ availability. In this regard, the proposed approach provides a seamless backup mechanism against single link failure with minimum communication delay based on the survivability model. In order to obtain an efficient solution, we have considered controller placement problem (CPP) as a multi-objective combinatorial optimization problem and solve it using two population-based meta-heuristic techniques such as: Particle Swarm Optimization (PSO) and FireFly Algorithm (FFA). For CPP, three metrics have been considered: (a) controller to switch latency, (b) inter-controller latency and (c) multi-path connectivity between the switch and controller. The performance of the algorithms is evaluated on a set of publicly available network topologies in order to obtain the optimum number of controllers, and controller positions. Then we present Average Delay Rise (ADR) metric to measure the increased delay due to the failure of the primary path. By comparing the performance of our scheme to competing scheme, it was found that our proposed scheme effectively improves the survivability of the control path and the performance of the network as well. <s> BIB031 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Meta-heuristic Algorithms Used in SDN <s> Telesurgery is a form of surgery in which a remote surgeon can perform surgical operation on a patient over a distance using robots. Due to human life is at stake, telesurgery is a high risk operation. Therefore, safety is one of the most important issues in telesurgery. Implementing appropriate mechanisms and protocols over communication network in order to transmit telesurgical traffics effectively, can improve the safety of telesurgery. The limitations of traditional IP-based protocols make it difficult to provide quality of service (QoS) for telesurgery. Fortunately, the advent of software defined network (SDN) facilitates to provide and guarantee QoS for most applications such as telesurgery. This paper investigates the feasibility of telesurgery over SDN, and proposes an SDN-based network architecture for the implementation of telesurgery. The proposed architecture considers all communication aspects of telesurgery and manages telesurgical traffics by using an efficient traffic engineering (TE) technique. To prove the effectiveness of our proposed architecture and TE method, we conducted extensive simulations for telesurgery application. Experimental results confirm that the proposed solution can be practically used for telesurgery and improve the performance of telesurgery in terms of QoS parameters. <s> BIB032
A large variety of meta-heuristic algorithms such as ant colony optimization BIB005 BIB015 BIB009 BIB006 BIB010 BIB016   BIB011 , evolutionary algorithms  BIB017 BIB018 , genetic algorithms  BIB019 BIB001 BIB002 BIB012 BIB020 BIB021 BIB007 BIB028 , particle swarm optimization  BIB022 BIB023 BIB024 BIB029 BIB030 , simulated annealing BIB003  BIB004 , bee colony optimizationbased BIB013 BIB014 , whale optimization BIB025 BIB026 , firefly optimization BIB031 , bat algorithm BIB008 , teaching-learning-based optimization BIB032 and grey wolf optimization BIB027 were used in SDN.