Abstract
In this paper, a stochastic traffic assignment model for networks is proposed for the study of discrete dynamic Bayesian algorithms. In this paper, we study a feasible method and theoretical system for implementing traffic engineering in networks based on Bayesian algorithm theory. We study the implementation of traffic assignment engineering in conjunction with the network stochastic model: first, we study the Bayesian algorithm theoretical model of control layer stripping in the network based on the discrete dynamic Bayesian algorithm theory and analyze the resource-sharing mechanism in different queuing rules; second, we study the extraction and evaluation theory of traffic assignment for the global view obtained by the control layer of the network and establish the Bayesian algorithm analysis model based on the traffic assignment; subsequently, the routing of bandwidth guarantee and delay guarantee in the network is studied based on Bayesian algorithm model and Bayesian algorithm network random traffic allocation theory. In this paper, a Bayesian algorithm estimation model based on Bayesian algorithm theory is constructed based on network random observed traffic assignment as input data. The model assumes that the roadway traffic distribution follows the network random principle, and based on this assumption, the likelihood function of the roadway online traffic under the network random condition is derived; the prior distribution of the roadway traffic is derived based on the maximum entropy principle; the posterior distribution of the roadway traffic is solved by combining the likelihood function and the prior distribution. The corresponding algorithm is designed for the model with roadway traffic as input, and the reliability of the algorithm is verified in the arithmetic example.
Highlights
IntroductionWith the development and popularity of internet technology, the internet has gradually affected every aspect of life, and various new application scenarios and network ecologies have emerged, promoting the rapid promotion and use of technologies such as big data and cloud computing [1]
With the development and popularity of internet technology, the internet has gradually affected every aspect of life, and various new application scenarios and network ecologies have emerged, promoting the rapid promotion and use of technologies such as big data and cloud computing [1].e development of network scale gradually grows, new network technologies are diversified, and the traditional network architecture has difficulties in adapting to the deployment of new technologies [2]
We study the problem of traffic distribution detection and identification in networks, draw on the advantages of security detection strategies in traditional networks, combine the characteristics of network randomness, make improvements to the shortcomings, and propose a multilevel phased hybrid detection method
Summary
With the development and popularity of internet technology, the internet has gradually affected every aspect of life, and various new application scenarios and network ecologies have emerged, promoting the rapid promotion and use of technologies such as big data and cloud computing [1]. When the scale of the network expands, the network structure becomes complex, and the deep coupling of control protocols and routing and Discrete Dynamics in Nature and Society forwarding devices limits the horizontal scalability and openness of the traditional network, leading to the increasingly complex task of network networking and management. A traffic distribution messaging mechanism on a network for the random network is proposed This convergence is not guaranteed in graphs with rings and its computation is very difficult for complex models with non-Gaussian continuity variables [6]. For this reason, a nonparametric belief propagation algorithm is proposed by combining the ideas of Monte Carlo and particle filtering for modeling uncertainty. A new multitask detection system based on convolutional neural networks can achieve automatic anomaly learning using deep learning methods without the need to detect many traffic feature attributes in the system. e previous research results outline the Bayesian algorithm theoretical system; the network algorithm analysis model in SDN network architecture is studied, and the network algorithm conflict model under the control layer separation architecture and the analysis mechanism of network resource competition is proposed; the scheduling mechanism of three queuing rules, namely, first in, first out, random fair queuing, and hierarchical password bucket, is established in SDN based on the network algorithm
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