Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Ant colony optimization 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> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Ant colony optimization 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> BIB002 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Ant colony optimization 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> BIB003 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Ant colony optimization 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> BIB004 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Ant colony optimization 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> BIB005 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Ant colony optimization 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> BIB006 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Ant colony optimization 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> BIB007
ACO has been widely used for solving various networking problems such as routing BIB001 BIB006 BIB003 BIB007 , load balancing BIB002 BIB004 , network security   and maximizing net-work utilization BIB005 . Dobrijevic et al. BIB001 presented an ACO approach for flow routing. The proposed model also employs quality of experience (QoE) estimation models and attempts to maximize the user QoE for multimedia services. The experimental results showed promising QoE improvements in compared with shortest path routing. Wang et al. BIB006 introduced two ACO-based algorithms for routing and spectrum assignment (RSA). The first one is ant colony optimization algorithm of minimum consecutiveness loss (ACO-MCL). The second one is ant colony optimization algorithm of maximum spectrum consecutiveness (ACO-MSC). The experimental results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Gao et al. BIB003 presented, CACO-RSP, a traffic engineering ACO-based approach to solve the routing rule space occupation problem for multiple unicast sessions. CACO-RSP algorithm considers both the local and global pheromone trail to guide the searching. The simulation results show that the proposed algorithm was successful in reducing the routing rule consumption. Di Stefano et al. BIB002 proposed, A4SND, a load-balancing algorithm with low complexity and hight scalability based on extending AAA algorithm, which is a modified version of ACO algorithm. A4SDN is different from the ACO-based routing algorithms in the following two points. First, the opposite interpretation of the pheromone trails. Second, the sub-path pheromone evaluation. The decision making policy in A4SND is based on pheromone laid on sub-paths, which makes it appropriate for on-line decision problems such as the routing. A4SND was emulated with the Mininet tool with two wellknown variants of the Dijkstra's shortest path algorithm. The experimental results showed that A4SDN was able to outperform other two variants of Dijkstra's shortest path algorithm in terms of network throughput, end-to-end latency and packet loss. Wang et al. BIB004 presented, LLBACO, a link load balancing algorithm based on ACO. LLBACO considers link load as main factor, whereas delay and pack-loss are the secondary factors. The experimental results showed that LLBACO has a better performance in balancing the network load and improving the QoS. Parsaei et al. BIB007 investigated a method for providing QoS for remote telesurgery applications. The proposed approach periodically collects statistics of network state and uses ACO for computing the best path between surgeon and patient. The proposed approach improved the average End-to-End delay, packet loss ratio and peak signal to noise ratio (PSNR) with values of 56.3%, 50.5% and 1.33% respectively. Chen et al.  proposed an ACO-based method for detecting low-rate distributed denial of service (LDDoS). The proposed method consists of three stages, an information heuristic stage, a multi-agent ACObased algorithm, and backward and forward search stages. The experimental results showed that the detection rate was more than 89% and the accuracy is greater than 83%. Liu et al.  introduced a defence mechanism of random routing mutation (RRM) in SDNs. The authors constructed an entropy matrix of network traffic characteristics and detected network anomalies. Routing mutation is triggered according to the anomaly detection results. The generation of a random routing path is formalized as a 0-1 knapsack problem, which is calculated by means of an enhanced version of ACO algorithm. The experimental results showed that the proposed approach was successful in defending against reconnaissance, eavesdropping, and DoS attacks. The main drawback of this approach was the increased burden on the controller. Yi et al. BIB005 used WiseAnt Colony Optimization (WACO) algorithm to solve the Bounded Forwarding-Rules Maximum Flow (BFR-MF) problem (i.e. limited resource problem in TCAM-based SDN switches). The experimental results showed that WACO can improve the network utilization and throughput by up to 16% on the premise of a certain level of QoS.