Visual identification of some regularities in packet network traffic
DOI:
https://doi.org/10.15587/1729-4061.2024.299002Keywords:
UDP, AR-estimation, moving window, packet intensity, long-term trend, high-frequency componentAbstract
Modern heterogeneous packet networks generate network traffic with a complex structure. In this article, the object of study is a time series. The total number of User Datagram Protocol (UDP) packets has reached 250242. According to analysts, the growth trend of traffic, including real-time applications, will continue and the volume of data will grow, which may lead to the formation of packet queues when processed by network devices. In this case, there may be losses in case of long queues. To solve this problem, a power spectrum assessment was carried out. The AR maximum entropy estimator has been shown to be more sensitive than the auxiliary Fourier estimator.
Accounting for non-stationarity by spectral methods is possible only through estimation in a sliding time window. Nine diagrams of spectral-temporal analysis of the original series, its increments, and the mixed series of increments were obtained: with default parameters, with small and large windows. Diagrams related to the original series reflect the dynamics of changes in data transmission intensity in the network; they show higher temporal resolution, indicating the presence of high-frequency components (noise) and the presence of low-frequency components (trend). Diagrams with increments describe signals of periodic components; changing the length of the window did not reflect the presence of noise or trend signs. Diagrams with mixed increments show that frequency components are uniformly distributed. The uniqueness of this work lies in the real measured data, and a distinctive feature of the obtained results is the visual examination of the complex traffic structure, allowing for the resolution of the investigated problem. Practical application of the results obtained can be applied in Quality of Service (QoS) management, resource planning, and network performance optimization
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Copyright (c) 2024 Sharafat Mirzakulova, Zhanar Ibrayeva, Saule Kuanova, Aisha Mamyrova, Bakyt Japparkulov, Ruslan Kamal
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