Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Particle swarm optimization 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> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Particle swarm optimization 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> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Particle swarm optimization 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> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Particle swarm optimization 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> BIB004 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Particle swarm optimization 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> BIB005
Used for routing  BIB001 , resource management BIB002 , multi-tenant virtual network customization BIB003 , multi-class routing BIB004 , and detection of DDoS attacks BIB005 . Awad et al.  proposed, PSOPR, a PSO-based powerefficient routing approach for solving the problem of flow table overflow. The experimental results showed that PSOPR can achieve more than 90% of the optimal network power consumption while it requires only 0.0045% to 0.9% of the optimal computation time in real-network topologies obtained from SNDlib (a library of test instances for Survivable fixed telecommunication Network Design). Moreover, PSOPR resulted in shorter routes than the optimal routes obtained by the optimization package CPLEX. Xiong et al. BIB001 proposed, PALM, power-aware lightpath management algorithm for traffic prediction in SDN-based elastic optical networks. The goal here is to avoid tearing down a light-path that becomes idle and takes into consideration reducing the switching power, which is required to re-establishment a light-path again in short time later. They also introduced a PSO-BP neutral network model to assist the PALM algorithm in accurately predicting future traffic demands and reducing the power consumption. More precisely, a PSO-BP neural network is used to predict the traffic load for every light-path within a particular time interval. PLAM achieved the power saving of 36% and 16% when compared with energy-efficient many-cast (EEM) algorithm and dynamic scheduling and distance-adaptive transmission (DS+DAT) algorithm respectively. Chang et al. BIB002 used GA and PSO for network allocation in 5G under NFV/SDN architecture. In terms of energy consumption GA-PSO approach achieved better results over both greedy strategy and OSFP approaches. In heavy load network environments, the proposed system can save energy nearly about 32% less than the OSPF. Their experimental results showed also a larger average link resource utilization in compared with OSPF and greedy strategy. Li et al. BIB003 introduced a PSO-Based virtual SDN customization for multi-tenant cloud services. Their experimental results showed that the PSO algorithm can significantly improve utilization rate of the underlying network bandwidth. Abdulqadder et al. BIB004 presented SecSDN-cloud, an integrated, secure, SDN and cloud-based architecture for providing enhanced quality of service (QoS) and more secure network. In SecSDN, the authors introduced an enhanced PSO multiclass (E-PSO) routing protocol, which takes three parameters into consideration namely: node congestion, link congestion and delay. The proposed PSO-based multi-class routing protocol included a data traffic classifier per-node to segregate traffic flows into QoS classes. Then it moves to the route selection process in which a node to check the QoS of other nodes. Another node is selected as the one hop neighbour for transmission, if it has a higher QoS value than the current node. SecSDN showed better results in terms of throughput when compared with OpenSec and AuthFlow. SecSDN showed better results in terms of throughput when compared with OpenSec and AuthFlow. In addition, it achieved better end-to-end delays when compared with OMC-RPL and a lower packet loss rate when compared with RPL based SDN and FlowDefender. Dayal and Srivastava BIB005 proposed a model for DDoS detection based on RBF-based neural network with PSO opti-mization. The author used the following features: average packets per flow, average bytes per flow, number of flows per second, average duration per flow, entropy of destination IP addresses per second, entropy of source IP address per second and entropy of IP protocol per second. The experimental results showed RBF-PSO achieved better accuracy and less training time when compared with NN-BP and NN-PSO.