Network Traffic Prediction for a Software Defined Network based Virtualized Network Functions Platform

Rankothge, Windhya; Gamage, Narmada; Dewwiman, H.G.H.; Ariyawansa, M.M.T.R.; Suhail, Sabah; Senevirathne, M.D.B.P.

doi:10.1109/icraie52900.2021.9703803

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The machine learning method FbProphet, developed by Facebook, has gained popularity as it aids mobile operators in efficient planning and costsaving investments, especially when compared to LSTM [12]. FbProphet has been utilized by [12,14,[25][26][27][28][29] for predicting cellular traffic growth in telecommunications networks in their respective studies. Not only for traffic, FbProphet has also been employed by [28] to predict revenue generated by telecommunications service providers in Ghana.…”

Section: Related Workmentioning

confidence: 99%

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Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

2023

IJIES

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The rapid growth of mobile internet usage, particularly in LTE networks, poses challenges for mobile operators in maintaining quality of service (QoS) and optimizing network design planning. Accurate traffic volume forecasting is crucial for network planning and effective resource allocation. This study aims to propose an enhancement to the accuracy of the holt's winter multiplicative seasonal (HWMS) method by applying the rolling forecast technique to achieve better forecasting results. Using a public dataset of 56 cells, the evaluation based on mean absolute percentage error (MAPE) reveals the following sequence of prediction errors: HWMS & rolling forecast (20.47%), HWMS only (30.06%), FbProphet (30.45%), and auto regressive integrated moving average (ARIMA) (31.52%). The cells are categorized as "Good" (59%), "Reasonable" (39%), and "Poor" (2%). The results indicate that the proposed method achieves a significantly higher percentage of cells in the "Good" category, with a 45% difference compared to HWMS without rolling forecast, which only achieved 14%. Moreover, the proposed method outperforms ARIMA by 50% and FbProphet by 37% in the same category. Furthermore, when applied to real data from a telecommunications company in Indonesia, the proposed method identified 11 cells that require solutions out of a total of 100 cells. In comparison, the ARIMA method identified 3 cells, FbProphet identified 12 cells, and HWMS without Rolling Forecast identified 9 cells. Thus, the company can provide solutions for the identified 11 cells without the need for excessive investment while still maintaining revenue potential.

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Section: Related Workmentioning

confidence: 99%

“…This approach should be based on the characteristics and patterns observed in the data. Sometimes, the FbProphet model can provide adequate predictions without the need for integration with K-means, as seen in the studies by [26,28,29]. Therefore, FbProphet is chosen as one of the comparative methods in this study.…”

Section: Related Workmentioning

confidence: 99%

Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

2023

IJIES

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ILBPS: An Integrated Optimization Approach Based on Adaptive Load-Balancing and Heuristic Path Selection in SDN

Ye,

Sun,

Guizani

2024

IEEE Internet Things J.

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Slope Deformation Prediction Based on MT-InSAR and Fbprophet for Deep Excavation Section of South–North Water Transfer Project

Ding

Lei

et al. 2022

Sustainability

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In the operation and maintenance of the South–North Water Transfer Project, monitoring and predicting the canal slope deformation quickly and efficiently is one of the urgent problems to be solved. To predict the slope deformation of the deep excavated canal section at the head of the canal. We propose a new idea of adopting the joint prediction of MT-InSAR and Fbprophet. Firstly, MT-InSAR monitoring technology was used to invert channel deformation using 88 Sentinel-1A orbit-raising image data with a time baseline from 2017 to 2019. The time-series deformation of nine monitoring points was also extracted, and it was found that the time-series curves of the cumulative deformation of the channel slope showed fluctuations. The Fbprophet algorithm was then used to train the prediction model in Python to predict the channel slope deformation over the next 365 days. Finally, the prediction results were compared with the MT-InSAR monitoring values to analyze the prediction accuracy and applicability of the Fbprophet algorithm for the slope deformation monitoring of the South–North Water Transfer Project. The results show that: the deformation rate of the slope of the deep excavation section is in the range of 10 mm/a to 25 mm/a, the maximum accumulated deformation is about 60 mm, and the slope of the excavation canal shows a lifting phenomenon; among the nine monitoring points, the minimum and maximum predicted values of deformation using the machine learning prediction model trained in this paper were 56 mm and 73 mm, respectively; comparing the predicted and monitored values, their correlation coefficients were 0.998 at the highest and 0.988 at the lowest, and the minimum and maximum values of RMSE (RootMean Square Error) were 0.72 mm and 2.87 mm, respectively. It shows that the prediction model trained by the Fbprophet algorithm in this paper applies to the prediction of slope deformation in the deep excavation section, and our prediction results can provide a data reference for disaster prevention and the sustainable development of the South–North Water Transfer Project.

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Network Traffic Prediction for a Software Defined Network based Virtualized Network Functions Platform

Cited by 3 publications

References 16 publications

Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

Forecasting LTE Network Traffic Using Hot-Winter’s Multiplicative Seasonal Method and Rolling Forecasting for Telecommunication Company Investment Optimization in Indonesia

ILBPS: An Integrated Optimization Approach Based on Adaptive Load-Balancing and Heuristic Path Selection in SDN

Slope Deformation Prediction Based on MT-InSAR and Fbprophet for Deep Excavation Section of South–North Water Transfer Project

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