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An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.

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To obtain better bandwidth and performance, the fifth generation (5G) cellular network is proposed to implement newgeneration cellular mobile communications for new applications such as the internet of things, big data, smart city and so on. However, due to multiple/dense cellular network structures and high data rate, the 5G cellular network holds high inter-cell interference (ICI) and lower energy efficiency. The soft frequency reuse (SFR) is introduced to reduce the inter-cell interference in multiple cellular networks (such as 5G cellular networks) with the orthogonal frequency division multiplexing in base stations. Then, we investigate the energy-efficient resource allocation problem in the 5G cellular network with SFR. To coordinate the ICI among adjacent cells, we introduce the interference pricing factor into the utility function. The energyefficient resource allocation problem is described as a Stackelberg game model. Because the sub-carrier assignment in the optimization process is an integer program which is very hard to be solved, we make a relaxation for the integer variable in the model and propose an iteration algorithm to obtain the Stackelberg game equilibrium solution. Simulation results show that the proposed method is feasible and promising.