To make ensure the quality of service of wireless Mesh networks and improve network performance, this paper aims at the problem of load imbalance caused by several existing main wireless Mesh network load balancing algorithms without fully considering the backbone network nodes and link states. A dynamic load balancing algorithm based on network calculus. First, use the depth-first traversal algorithm to construct available paths; then, establish the queuing delay and data backlog model of nodes, and combine the comprehensive calculation of path reliability with nodes and links to obtain a set of reliable paths; finally, calculate the allocation data of different paths The weight of the packet, and distribute data packets on demand according to the weight. Simulation results show that compared with traditional AODV protocol, HWMP protocol and improved LB-HWMP protocol, the algorithm proposed in this paper is superior in delay and throughput.
Aiming at the problems that the existing network flow to QoS (Quality of Service) class aggregation lacks flexibility and the related clustering methods have many iterations and slow clustering, a dynamic flow clustering method was proposed. Using the method of data field clustering and rough set theory, the network flows are clustered according to the QoS attribute value of network flows, and the membership degree of statistics is used to cluster network flows flexibly, so that network flows can be aggregated flexibly. With each data point as the field source, the relationship of each data point in the field is established, and the clustering speed is improved. Experimental results show that the algorithm can play a certain dynamic regulation effect in both low and high load conditions.
Aiming at the problems of uneven distribution of ground users, reliability fluctuation of nodes and links and frequent switching of controller groups in software-defined satellite networks, a multi-controller deployment strategy for LEO satellites based on nearest neighbor propagation is proposed. The strategy aims to reduce the delay, balance the network load, improve the reliability of nodes and links and extend the effective duration of the controller group. The control domain is divided by the nearest neighbor propagation clustering algorithm and the controller group is selected. Then the simulated annealing algorithm is used to iteratively select a better performance scheme. Experiments show that the algorithm can effectively reduce the delay in the control domain, improve the link reliability, and ensure the stability of the controller group under the condition of guaranteeing the load balance of the whole network.
Accurately estimating the self-similar characteristics of time series has great application value for data analysis, prediction and system control. Because of the high mutability of time series, the existing Hurst parameter estimation methods fail to describe the local self-similarity of time series and consider its dynamic change, resulting in low estimation accuracy. Therefore, this paper proposes a time-varying Hurst parameter estimation method based on dynamic step size and overlapping windows (DSSOW-H). The method introduces to the dynamic step size based on the existing sliding window time-varying Hurst parameter estimation method and adjusts the sudden change of data through the change of dynamic step size, in order to effectively estimate the self-similar characteristics of local data when it suddenly changes. MATLAB is used to generate artificial FGN sequences with self-similar characteristics. At the same time, this method compare with the periodic diagram method, R/S method, variance-time diagram method, sliding window time-varying Hurst parameter estimation method (SWTV-H). According to the experimental results, the relative error of DSSOW-H is reduced by 53% compared with SWTV-H, and the relative error of DSSOW-H is 0.9875% when applied to satellite network traffic. Therefore, it is proved that the method in this paper is effective in improving the estimation accuracy and reducing the estimation error when the data has abrupt changes.
In view of the complex and polymorphic structure of satellite networks, this paper proposes a K-Terminal communication reliability modeling method of satellite networks based on fault tree and Bayesian. Firstly, the fault tree is established according to the available path information that meets the QoS constraints, and then the corresponding relationship between the fault tree and Bayesian network is summarized. Finally, it is expressed with the help of Bayesian network. Combined with the conditional probability table, the two-way evaluation of satellite network is carried out by using Bayesian theorem, and the k-end path reliability of satellite link in different states is obtained. The results show that the model can not only improve the problem of multi-state network modeling, but also reduce the complexity of previous modeling.
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