Hidden-Markov-based Internet network delay forecasting method

Abstract

The invention discloses a hidden-Markov-based Internet network delay forecasting method in the technical field of network delay forecasting. The method comprises the following steps of: acquiring observable status and an observable status sequence according to a historic delay data set and preset delay forecasting precision; clustering the historic delay data set by a K-Means clustering method, computing the error of the historic delay data set under different k values, and confirming an initial value according to the error of the historic delay data set under the different k values; estimating hidden Markov parameters under the different k values, computing a hidden Markov bayes information criterion value under each k value according to the hidden Markov parameters under the different k values, and selecting the k value, which corresponds to the minimum hidden Markov bayes information criterion value, as an optimal hidden status number k-best; and forecasting future delay according to the observable status and the optimal hidden status number k-best. According to the method disclosed by the invention, the rule of the delay data set and the characteristic of the Internet network can be exactly expressed, and the accuracy of forecasting of the future observable status is high.

Description

technical field [0001] The invention belongs to the technical field of network time delay prediction, in particular to a method for predicting Internet network time delay based on Hidden Markov. Background technique [0002] Among Internet-based applications, some applications are not sensitive to Internet network delay, but many applications have higher requirements on Internet network delay. For applications with high latency requirements, there are generally two methods for predicting latency: one is to perform fitting based on the relationship between latency data to predict future latency; the other is to build a network model of the Internet , to realize the prediction of the delay. Compared with the former method, the latter method has a better predictive effect, because the latter not only can include the regularity between time-delay data, but also can better reflect the current network status and the status of the network in the future. and delay conditions. [...

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Problems solved by technology

[0002] Among Internet-based applications, some applications are not sensitive to Internet network delay, but many applications have higher requirements on Internet network delay

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