Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Whale optimization algorithm 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> BIB001 </s> Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview <s> Whale optimization algorithm 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> BIB002
Farshin and Sharifian BIB001 proposed, MAP-SDN, a framework for assignment and provisioning for SDN-based cloud data centers with different classes of service. The authors compared the performance of WOA with PSOGSA algorithm. PSOGSA, combines the exploitation of particle swarm optimization (PSO) with exploration of gravitational search algorithm (GSA). The experimental results showed that WOA was more accurate and it can reach better results with lesser computation time. WOA also has been used for achieving clustering-based routing for heterogeneous, randomly distributed and dense IoT networks BIB002 . The proposed approach consisted of two stages: set-up stage and transmission stage. At the set-up stage, the SDN controller divides the sensing area into virtual zones for balancing the number of cluster heads (CH). Then at the transmission stage the controller uses WOA for each virtual zone to determine the optimal set of CHs in that particular virtual zone. The simulation results showed that the proposed approach can minimize the power consumption in compared to conventional routing protocols. In addition, the total number of packets received by the sink throughout the simulation time has improved by approximately 55% in compared to conventional clustering protocols, and 20% compared to the optimization based (PSO) clustering protocol.