Software defined networking (SDN) is a modern and upcoming standard in today's network as it centralizes the network intelligence by separating the control plane from the forwarding plane. Placing a controller in an appropriate location and minimizing the switch to controller (SC) latency are important factors in SDN. In this article, we proposed a mathematical algorithm to form the clusters and placed one controller in each cluster to shorten the worst-case SC latency. We have proposed a technique called the "$-method" by using the distance matrix of the network nodes and placed a "$" whenever we have found the matrix element is greater than a given maximum distance. Initially, the maximum distance is calculated using the center of mass method that we have developed for minimizing the average SC latency. Our method guarantees that it will generate the minimum worst-case SC latency efficiently that one could ever achieve for a different number of controller placement. Simulation has been done under some real-world network topologies from the dataset of Internet Topology Zoo. Our result shows that the "$-method" performs better compared with other existing algorithms in terms of worst-case SC latency minimization with less number of controllers. Further, we have analyzed our method for the failure mode of a controller by assigning the switches of that failed controller to its nearest controllers which shows that our method also performs better in terms of network fault tolerance and improves network resilience.

Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it "Channel quality factor (CQF)" or 'Z'. The other parameter known as "Communication expiration time" or 'T' together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives ∼ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to ∼ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

Software-defined network (SDN) is a programmable networking paradigm that enables logically centralized network management. In order to overcome the shortcomings of traditional networks, SDN has come into the picture. SDN is a recent and fast-forwarding technique that involves separating the network's control plane from the data plane. This network also allows us to directly communicate with the applications through application programming interfaces. A network administrator can directly modify and update the routing policies and rules in SDN. Apart from the advantages given by the SDN, it has also been found that separation of control plane directly affects the network performance.Finding the optimum number of controllers and their locations to improve the performance is one of the fundamental research problems in SDN which is also commonly known as the controller placement problem (CPP). In this article, we have addressed the CPP in SDN in detail and investigated the optimization objectives that influence CPP. The state-of-the-art of this article is to review almost all the optimization objective along with their mathematical formulation and survey the CPP solutions proposed by the other researchers'. Classified the CPP into three parts based on the objectives and solutions of CPP and reviewed the impact of these objectives on network performance. This article gives a comprehensive overview of current strategies on CPP in SDN and at last, we give some research issues that can be further explored by the researchers.

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Summary A network incorporates nodes, and each node can communicate with each other through some links. An efficient way to maintain the communication between nodes is to divide a network into several subnetworks, called clusters. We have developed a new clustering algorithm and applied our method for the controller placement in software‐defined networks (SDNs). Placing a controller in its appropriate location by balancing the loads and optimizing the latency even in case of a failure scenario becomes a challenging task. Thus, we have proposed a multi‐controller placement algorithm that can minimize the average (Sw‐Co) latency in such a way that the network switches are fairly distributed over clusters. This distribution helps to balance loads of switches among controllers even in the case of a controller failure scenario. We have also simulated three other existing algorithms for comparison. Experiment results show that our algorithm is a cost‐effective solution as compared with other existing algorithms. We have also shown that our proposed method balanced loads of switches between controllers in a SDN network and generates lower average (Sw‐Co) latency with and without a controller failure.