In Software Defined Network (SDN), the controller layer that is separated from the data layer is responsible for all system functionalities. However, centralized solutions suffer from single-point-of-failure and bottleneck problems. Several controllers are used to increase availability and performance in large networks to solve the aforementioned problems. One of the main concerns is finding the optimal number of controllers and their locations, which is known as an NP-hard problem. To do this, in this paper, in addition to presenting an efficient algorithm based on Garter snake algorithm (GSO), a new statistical analysis for determining the number of controllers is figured out. پرونده مقاله

Link Prediction (LP) is one of the main research areas in Social Network Analysis (SNA). The problem of LP can help us understand the evolution mechanism of social networks, and it can be used in different applications such as recommendation systems, bioinformatics, and marketing. Social networks can be shown as a graph, and LP algorithms predict future connections by using previous network information. In this paper, a multi-wave cellular learning automaton (MWCLA) is introduced and used to solve the LP problem in social networks. The proposed model is a new CLA with a connected structure and a module of LAs in each cell where a cell module’s neighbors are its successors. In the MWCLA method for improving convergence speed and accuracy, multiple waves have been used parallelly in the network. By using multiple waves, different information of the network can be considered for predicting links in the social network. Here we show that the model converges upon a stable and compatible configuration. Then for the LP problem, it has been demonstrated that MWCLA produces much better results than other approaches compared to some state-of-the-art methods. پرونده مقاله

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. پرونده مقاله