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Study on network traffic prediction techniques | IEEE Conference Publication | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This article provides an in-depth study on the performance of various network traffic predictors, such as ARIMA, FARIMA, ANN and wavelet-based predictors.
2. The article discusses the computational complexity of each predictor and compares their performance with MSE, NMSE and computational complexity by simulating the predictors on four wireless network traffic traces.
3. The article concludes that ANN is the most suitable online predictor for dynamic bandwidth allocation, congestion control, etc., while FARIMA is the best offline predictor for network design.
Article analysis:
The article provides a comprehensive overview of various network traffic prediction techniques and their respective performance metrics. It is well written and clearly explains each technique in detail. The authors provide evidence to support their claims by citing relevant research papers and providing examples from actual wireless traces.
However, there are some potential biases in the article that should be noted. For example, the authors focus mainly on parametric predictors (ARIMA and FARIMA) and nonparametric predictors (ANN and wavelet-based). They do not discuss other types of predictors such as Bayesian or Markov models which could also be used for predicting network traffic. Additionally, they only consider four different types of wireless traces when evaluating the performance of these techniques; it would have been beneficial to include more traces to get a better understanding of how these techniques perform in different scenarios.
Furthermore, there is no discussion about possible risks associated with using these prediction techniques or any counterarguments to their conclusions. Additionally, there is no mention of any promotional content or partiality in the article which could lead readers to believe that all presented information is unbiased and objective.
In conclusion, this article provides a comprehensive overview of various network traffic prediction techniques but does not explore all possible options or discuss potential risks associated with using them. It would have been beneficial if the authors had included more traces when evaluating these techniques as well as discussed possible counterarguments to their conclusions and explored potential risks associated with using these prediction techniques.