Abstract
Kurumsal ağlarda verinin kullanılması ve işlenmesi konusunun önemi gelişen global dünyada her geçen gün artmaktadır. Sistemlerin yapıları gereği veri kullanımı, bağlantılı yollardan ağ trafik yoğunluğunu arttırmaktadır. Verinin yoğunluğundaki artış, sistem altyapıları ve performans değerlerine etki eden en önemli faktörlerden biridir. COVID-19 başlamadan önceki veri kullanım miktarlarının kurumsal ağlardaki, ağ trafiğinin kullanım yoğunluk oranlarına olan etkisi ve pandemi sürecinin başlamasıyla gelişen ağ trafik yoğunluğunun etkisinin ölçülmesi ve değerlendirilmesi bu yapının daha iyi anlaşılmasını sağlamaktadır. Wavelet Transform (Dalgacık Dönüşümü) ve Continuous Wavelet Transform (Sürekli Dalgacık Dönüşümü) kullanılarak, sistemden elde edilen veri setine dayalı yapılan bir uygulama ile veri yoğunluğunun zamansal değişimi analiz edilmiştir. Wavelet Transform uygulanan COVID-19 öncesi ve sonrasında işlenen sinyal verilerinin (Mbps veya Gbps) analiz çalışması sonrası oluşturulan grafiksel veriler incelenip, yorumlanmıştır. COVID-19 öncesinde 8791 veri varken, COVID-19 sonrasında ise 16167 veri göz önüne alınmış bulunmaktadır. Ayrıca, ağ trafiği incelendiğinde, pandemi döneminde arasında %141 kadar artmışken, ortalamada gözlemlenen ağ trafik hızı ise %79 artmıştır.
References
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Abstract
The importance of using and processing data in corporate networks is increasing day by day in the developing global world. Due to the nature of the systems, the use of data increases the network traffic density from the connected roads. The increase in data density is one of the most important factors affecting system infrastructures and performance values. Measuring and evaluating the effect of data usage amounts before the start of COVID-19 on the usage density rates of network traffic in corporate networks and the effect of network traffic density that develops with the onset of the pandemic process provides a better understanding of this structure. By using Wavelet Transform and Continuous Wavelet Transform, the temporal variation of data density was analyzed with an application based on the data set obtained from the system. The graphical data created after the analysis of the signal data (Mbps or Gbps) processed before and after the COVID-19 by using Wavelet Transform were examined and interpreted. a specific results, statistical magnitudes and different scale effects were analized. While there were 8791 data before COVID-19, 16167 data were taken into account after COVID-19. In addition, when the network traffic was examined after the pandemic period, it increased %141 times, higher thans the speed recorded before pandemic period similarly the average network traffic speed is %79 times.
References
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- Aslan, Z., Dokmen, F., Feoli, E., Siddqi, A, H., (2019). Assesment of Water Quality Model Using Fuzzy Logic System: A Case Study of Surface Water Resources in Yalova of Turkey, NOVA Science Publishers Inc. New York, ss.103-118.
- Batar, H., (2005). EEG işaretlerinin dalgacık analiz yöntemleri kullanılarak yapay sinir ağları ile sınıflandırılması, Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Sütçü İmam Üniversitesi
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- Daubechıes, I., (1990). The wavelet transform time-frequency localization and signal analysis., IEEE Transactions on Information Theory, pp. 961-1004
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- Faust, O., Acharya U.R., Adeli, H., Adeli, A., (2015). Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis, British Epilepsy Association, vol. 26, pp.56-64
- Guo M.F., Yang, N, C., You, l, X., (2018). Wavelet-transform based early detection method for short-circuit faults in power distribution networks, International Journal of Electrical Power & Energy Systems, vol. 99, pp. 706–721.
- Graps, A., (1995). An Introduction to Wavelets, IEEE Computational Secience And Engineering, vol. 2, pp. 1-18.
- Kaiser, G., (2010). A Friendly Guide to Wavelets (Modern Birkhäuser Classics), Cambridge, Published by Birkhäuser, 2011th edition.
- Kim, S.S., Reddy, A.l.N., Vannucci, M., (2004). Detecting traffic anomalies using discrete wavelet transform, International Conference ICOIN, pp. 951-961.
- Lindsay, R.W., Percival, D.B., Rothrock, D.A., (1996). The discrete wavelet transform and the scale analysis of the surface properties of sea ice., IEEE Transactions on Geoscience and Remote Sensing, pp. 771-787
- Meral, T., (2013). Veri Analizinde Dalgacık Teorisinin Etkinliği, Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Matematik Anabilim Dalı, Kocaeli Üniversitesi.
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- Suter, B., (1997). Multirate and Wavelet Signal Processing, USA, Academic Press, vol. 8 1st Edition.
- Türkmenoğlu, V., (2006). Güç elektroniği devrelerinin bilgisayar destekli çözümlenmesinde dalgacık yaklaşımının incelenmesi, Doktora Tezi, Fen Bilimleri Enstitüsü, Ondokuz Mayıs Üniversitesi.
- Xiaoyu, T., Lu, L., Ying, L., (2012). A New Generation Theory and Technology of Mobile Fusion Network, Posts and Telecommunications Press, vol. 11, pp. 101-103.
- Zhang, J., Sun, H., Sun, Z., Dong, W., Dong, Y., (2019). Fault diagnosis of wind turbine power converter considering wavelet transform, feature analysis, Judgment and BP neural network, IEEE Access, vol. 7.
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Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | April 19, 2023 |
| Submission Date | June 29, 2022 |
| Published in Issue | Year 2022 Volume: 17 Issue: 66 |
