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Understanding the dynamic traffic and usage characteristics of data services in cellular networks is important for optimising network resources and improving user experience.Recent studies have illustrated traffic characteristics from specific perspectives,such as user behaviour,device type,and applications.In this paper,we present the results of our study from a different perspective,namely service providers,to reveal the traffic characteristics of cellular data networks.Our study is based on traffic data collected over a five-day period from a leading mobile operator’s core network in China.We propose a Zipf-like model to characterise the distributions of the traffic volume,subscribers,and requests among service providers.Nine distinct diurnal traffic patterns of service providers are identified by formulating and solving a time series clustering problem.Our work differs from previous related works in that we perform measurements on a large quantity of data covering 2.2 billion traffic records,and we first explore the traffic patterns of thousands of service providers.Results of our study present mobile Internet participants with a better understanding of the traffic and usage characteristics of service providers,which play a critical role in the mobile Internet era.
Understanding the dynamic traffic and usage characteristics of data services in cellular networks is important for optimizingising network resources and improving user experience.Recent studies have illustrated traffic characteristics from specific perspectives, such as user behavior, device type, and applications.In this paper, we present the results of our study from a different perspective, namely service service, to reveal the traffic characteristics of cellular data networks. Your study is based on traffic data collected over a five-day period from a leading mobile operator’s core network in China. propose a Zipf-like model to characterise the distributions of the traffic volume, subscribers, and requests among service providers. Line distinct diurnal traffic patterns of service providers are identified by formulating and solving a time series clustering problem. Our work differs from previous related works in that we perform measurements on a large quantity of data covering 2.2 billion traff ic records, and we first explore the traffic patterns of thousands of service providers. Results of our study present mobile Internet participants with a better understanding of the traffic and usage characteristics of service providers, which play a critical role in the mobile Internet era.