Author_Institution :
Coll. of Inf. Sci. & Electron. Eng., Zhejiang Univ., Hangzhou, China
Abstract :
Mobile instantaneous messaging (MIM) services significantly facilitate personal and business communications, inevitably consume substantial network resources, and potentially affect the network stability. In this paper, we aim to understand the traffic nature of MIM in cellular networks. Specifically, in order to reach credible conclusions, our research takes account of practical measurement records of MIM services from China Mobile at two different levels. First, a data set of individual message level (IML) traffic is exploited and reveals power-law distributed message length and lognormal distributed interarrival time, the heavy-tailness of which completely diverts from the geometric model and the exponential model recommended by the 3rd generation partnership project (3GPP). Second, another data set considers the statistical pattern of aggregated traffic within one whole base station, and demonstrates the accuracy of α-stable models for the aggregated traffic. Furthermore, it verifies that the α-stable models are suitable for characterizing the traffic in both the conventional fixed core networks and the cellular access networks. At last, with the aid of the generalized central limit theorem, we build up a theoretical relation between the distributions of IML traffic and aggregated traffic.
Keywords :
3G mobile communication; cellular radio; electronic messaging; telecommunication traffic; α-stable models; 3GPP; 3rd generation partnership project; China mobile; IML traffic; MIM services; aggregated traffic; cellular access networks; cellular networks; conventional fixed core networks; individual message level traffic; lognormal distributed interarrival time; mobile instantaneous messaging services; power-law distributed message length; practical measurement records; statistical pattern; traffic nature; Data models; Distribution functions; Fitting; Instant messaging; Mobile communication; Mobile computing; Random variables; $alpha $ -stable models; Mobile instantaneous messaging; Wechat/Weixin; cellular networks; heavy-tailed distributions; traffic distribution;