software-defined networks (SDNs) are an emerging area in computer networks, enabling efficient resource management in the network by decomposing data and control plans. In SDNs, network controllers acting as the network operating systems, are responsible for serving application programs. Since a control plan consists of several controllers, the placement of network controllers is a challenging issue in complex networks. While the literature has explored the number of controllers and their placement in the network, several fundamental parameters remain unexplored. Therefore, this topic remains open for more investigations. In this paper, we survey the work in the literature on controller placement in SDNs and introduce the research challenges in this area. Additionally, we present potential future research directions to advance this field.

As a growing of IoT devices, new computing paradigms such as fog computing are emerging. Fog computing is more suitable for real-time processing due to the proximity of resources to IoT layer devices. Service providers must dynamically update the hardware and software parameters of the network infrastructure. software defined network (SDN) proposed as a new network paradigm, whose separate control layer from data layer and provides flexible network management. This paper presents a software-defined fog platform to host real-time applications in IoT. Then, we propose a novel resource allocation method. This method involves scheduling multi-node real-time task graphs over the fog to minimize task execution latency. The proposed method is designed to benefit the centralized structure of SDN. The simulation results show that the proposed method can find near to optimal solutions in a very lower execution time than the brute force method.

In recent years, software defined networks (SDN) have been raised as a promising approach to improve the network programmability and management of computer networks. It consists in separating the control plane from the data plane and centralizing the control part of the network. Due to the rapid growth of computer networks in terms of number of switches and the amount of transiting traffic, the distributed architecture with centralized view on the network has been designed for control plane, enhancing the scalability, availability, fault tolerance and reliability. In such a distributed architecture, the load balancing between controllers plays an important role towards the optimal use of networking resources. To address the aforementioned challenges, we propose a stable distributed solution for load balancing between controllers in software defined networks. The proposed solution collects information on the amount of load of controllers and their corresponding switches. Based on this knowledge, the controller with the highest overload migrates the switch leading to the best enhancement in load balancing of the network to the least-loaded controller, if the network load is not balanced and the migration benefit is significant compared to its cost. The proposed solution inhibits simultaneous migrations triggered by distributed controllers to avoid cascade re-migrations and ensures the network stability. The results of the test-bed study of the proposed approach show that out solution outperforms other counterparts up to 15% in terms of average memory consumption, 50% in terms of controller traffic throughput and 70% in terms of processing time of the overloaded controllers.

software-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network. It results in a flexible network with centralized management in which network parameters can be well controlled. Due to the increasing number of users, the emergence of new technologies, the explosive growth of network traffic, meeting the requirements of quality of service and preventing underload or overload of resources, load balancing in software-based networks is of substantial importance. Load imbalance increases costs, reduces scalability, flexibility, efficiency, and delay in network service. So far, a number of solutions have been proposed to improve the performance and load balancing in the network, which take into account different criteria such as power consumption and server response time, but most of them do not prevent the system from entering the load imbalance mode and the risks of load imbalance. In this paper, a predictive load balancing method is proposed to prevent the system from entering the load imbalance mode using the Extreme Learning Machine (ELM) algorithm. The evaluation results of the proposed method show that in terms of controller processing delay, load balance and response time, it performs better than CDAA and PSOAP methods.

Nowadays, multimedia networks on the Internet have become a low-cost and efficient alternative to PSTN. Multimedia transfer applications on the Internet are becoming more and more popular. This connection consists of two phases: signaling and media. The signaling phase is performed by SIP proxies and the media phase by network switches. One of the most important challenges in multimedia networks is the overload of SIP proxies and network switches in the signaling and media phases. The existence of this challenge causes a wide range of network users to face a sharp decline in the quality of service. In this article, we model the routing problem in multimedia networks to deal with the overload. In this regard, we present a technology-based method of software-based networks and a mathematical programming model in multimedia networks. The proposed method is simulated under various scenarios and topologies. The results investigate that the throughput and resource consumption has improved.

Unlike conventional wireless sensor networks which are designed for a specific application, software-defined Wireless Sensor networks (SDSN) can embed multiple sensors on each node, defining multiple tasks simultaneously. Each sensor node has a virtualization program which serves as a common communication infrastructure for several different applications. Different sensor applications in the network can have different target functions and decision parameters. Due to the resource constraints of sensor network nodes, the multiplicity and variety of tasks in each application, requirements for different levels of quality of service, and the different target functions for different applications, the problem of allocating resources to the tasks on the sensors is complicated. In this paper, we formulate the problem of allocating resources to the sensors in the SDSN with different objective functions as a multi-objective optimization problem and provide an effective solution to solve it.

Service Function Chaining is an architecture for orchestrating network services that assign choice to the network. This architecture is essentially a policy structure that should form the proper chain of services. Managing these networks is susceptible to error due to the combination of services with dedicated configurations. Accordingly, solutions will be needed to provide an appropriate ambiance for such a situation. Therefore, before running, the chains must be fully controlled, which requires the definition of chaining rules. Among the issues raised in this architecture are: checking the accuracy of the chains, as well as reducing the number of combinations of service chains. To solve these issues, the grammar is used in this paper. In this way, based on the scenarios in the Internet Engineering Task Force, they first create them and then their grammar is obtained using Regular Expressions and Finite Automaton. Subsequently, using the Cocke– Younger– Kasami algorithm, the grammar evaluation is performed and the number of combinations of services is also shown. The results show that this grammar can be verified by checking the service chain and also significantly reducing the number of combinations of service chains.

The software defined network (SDN) is a new computer architecture, where the central controller is applied. These networks rely on software and consequently, their security is exposed to different attacks through different components therein. One type of these attacks, which is the latest threat in computer network realm and the efficiency therein, is called the distributed denial of services (DDoS). An attempt is made to develop an attack-detector, through a combined statistical and machine learning method. In the statistical method, the entropy, based on destination IP and normal distribution in addition to dynamic threshold are applied to detect attacks. Normal distribution is one of the most important distributions in the theory of probability. In this distribution the entropy average and standard deviation are effective in attack detection. As for the learning algorithm, by applying the extracted features from the flows and supervised classification algorithms, the accuracy of attack detection increases in such networks. The applied datasets in this study consist of: ISCX-SlowDDoS2016، ISCX-IDS2012, CTU-13 and ISOT. This method outperforms its counterparts with an accuracy of 99. 65% and 0. 12 false positive rate (FPR) for the UNB-ISCX dataset, and with an accuracy of 99. 84% and 0. 25 FPR for CTU-13 dataset.