software-defined network

Software-defined networking (SDN) is a network structure where the control and data planes are separated. In traditional SDN, a single controller was in charge of control management, but this architecture had several constraints. To address these constraints, it is advisable to incorporate multiple controllers in the network. Selecting the number of controllers and connecting switches to them is known as the controller placement problem (CPP). CPP is a key hurdle in enhancing SDNs. In this paper a meta-heuristic algorithm called Honey Badger Algorithm (HBA), is used to determine the optimal alignment between switches and controllers. HBA is modified using genetic operators (GHBA). The assessments are conducted with a diverse range of controllers on four prominent software-defined networks sourced from the Internet Topology Zoo and are compared to numerous algorithms in this field. It is noted that GHBA outperforms other competing algorithms in terms of end-to-end delay and energy consumption.

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.

Software-defined networking is a new network model proposed to solve the complexity of traditional network problems and facilitate dynamic network operation and management. The separation of the control plane from the data plane is the main idea of software-defined networks. Controllers are the operating system of software-defined networks and are responsible for managing the entire network. It is essential to locate controllers appropriately to have a balanced topology while guaranteeing low latency. In this work, a metaheuristic algorithm is used for controller placement. First, the problem is formulated, and the network is partitioned by a clustering algorithm. Then, the seagull optimization algorithm is used to determine a suitable place for the controller in each network partition dynamically. Simulations are performed on the standard network topology from the internet topology zoo dataset to evaluate the proposed method. Simulation results reveal that the proposed method performs well in case of delay and load balancing compared with the state-of-the-art optimization algorithms.

In Software Defined Network (SDN), controller plane is separated from the data plane simplifying management. In these networks, data forwarding cannot be conducted just one controller. Therefore, it is needed to use multiple controllers in control plane. Since, switch-controller propagation delays and inter-controller latencies affect the performance, the problem of determining appropriate number of controllers as well as their suitable locations are two main challenges, which are known as NP-Hard. In this paper, a new clustering method based on K-means, K-Harmonics means and firefly algorithm named CPP-KKF is proposed for controller placement in SDN. Result obtained by CPP- KKF algorithm is benefitted by the advantages of all techniques. The proposed algorithm is evaluated on four topologies of TopologyZoo with different scales, that include Aarnet, Colt, Cognet, and DFN and the conducted simulations demonstrate that the proposed solution outperforms K-means, K-means++, Firefly and GSO algorithms in terms of aforementioned performance issues.

Software-Defined Network (SDNs) is a decoupled architecture that enables administrators to build a customizable and manageable network. Although the decoupled control plane provides flexible management and facilitates the task of operating the network, it is the vulnerable point of failure in SDN. To achieve a reliable control plane, multiple controller are often needed so that each switch must be assigned to more than one controller. In this paper, a Reliable Controller Placement Problem Model (RCPPM) is proposed to solve such a problem, so as to maximize the reliability of software defined networks. Unlike previous works that only consider latencies parameters, the new model takes into account the load of control traffic and reliability metrics as well. Furthermore, a near-optimal algorithm is proposed to solve the NP-hard RCPPM in a heuristic manner. Finally, through extensive simulation, a comprehensive analysis of the RCPPM is presented for various topologies extracted from Internet Topology Zoo. Our performance evaluations show the efficiency of the proposed framework.

Software defined network is a new computer network architecture who separates controller and data layer in network devices such as switches and routers. By the emerge of software defined networks, a class of location problems, called controller placement problem, has attracted much more research attention. The task in the problem is to simultaneously find optimal number and location of controllers satisfying a set of routing and capacity constraints. In this paper, we suggest an effective solution method based on the so-called Iterated Local Search (ILS) strategy. We then, compare our method to an existing standard mathematical programming solver on an extensive set of problem instances. It turns out that our suggested method is computationally much more effective and efficient over middle to large instances of the problem.