Abstract
The explosion of traffic brings the challenges for Internet Service Providers (ISPs) to make a profit with the high cost of infrastructure and increased competition. This calls for economic mechanisms that can enable providers to allocate on-demand resources through the prediction of traffic volumes and adjust the price. In this paper, we analyze the network traffic pattern of mobile data and make an accurate prediction of traffic volumes through ARIMA and LSTM. Based on the analysis, we then suggest a scalable price strategy for ISPs to satisfy the various requirements of customers.
Highlights
According to Cisco annual internet report, mobile users will increase to 5.7 billion, mobile connections will increase to 13.1 billion, and mobile traffic volume is estimated to reach almost one zeta-byte by 2023
In determining the optimal parameters for our Auto-regressive Integrated Moving Average (ARIMA) model, this study uses a few estimators as reference
It is found that the Long Short-Term Memory neural networks (LSTM) is capable of predicting the traffic with a small difference between the actual and predicted value
Summary
According to Cisco annual internet report, mobile users will increase to 5.7 billion, mobile connections will increase to 13.1 billion, and mobile traffic volume is estimated to reach almost one zeta-byte by 2023. Heikkinen discussed the issue of optimal quality of service and the optimal linear pricing mechanism in the multi-service network [4] These studies mainly focus on the maximization of social welfare and the case of small demand. They have not considered the customers’ own interests and usage patterns and ignored the actual value of consumed network resources. ISPs can promote this novel service price strategy to balance resource utilization, optimize user experience, and attract new customers with customer-made packages based on the prediction of traffic volumes.
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