Modeling and Mining the Temporal Patterns of Service in Cellular Network  

作　　者：Sun Weijian Qin Xiaowei Wei Guo 

机构地区：[1]Key Laboratory of Wireless-Optical Communications, Chinese Academy of Sciences, School of Information Science and Technology,University of Science and Technology of China

出　　处：《China Communications》2015年第9期11-21,共11页中国通信（英文版）

基　　金：supported by the National Basic Research Program of China (973 Program: 2013CB329004)

摘　　要：Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service（Qo E） support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator＇s network in China. We propose a Zipf＇s ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method（Self-Organizing Map combined with kmeans） is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.

关 键 词：big data cellular network data mining hadoop SOM cluster service 

分 类 号：TN929.5[电子电信—通信与信息系统] TP311.13[电子电信—信息与通信工程]