Characterizing and Modeling Microblog Traffic in Cellular Data Network Based on Massive Data Analysis

Abstract:

In this paper, we present an approach to characterize and model microblog traffic in cellular data network. In contrast to previous methods, our approach is based on the cloud computing platform and the cluster system, including the Hadoop Distributed File System (HDFS) and the parallel processing software framework MapReduce. Whats more, we focus on the contrast of Sina and Tencent microblogs. We analyze the features of microblog traffic in four aspects of increasing details, which are (i) traffic diurnal pattern, (ii) modeling the traffic distribution, (iii) user distribution, (iv) diversity usage of microblogs. This approach of analyzing microblog traffic comprehensively is probably the most important contribution of this paper. Furthermore, our approach has two important features. First, the massive mobile subscriber data we used in our experiments was collected from a commercial Internet Service Provider (ISP) covering an entire province in Southern China. Therefore, it ensures the results indicate the true characteristics of microblog traffic in network. Second, we investigate that the microblog traffic fits with the power law distribution. We demonstrate the electiveness of our approach on three real datasets. Our results are important for cellular network operators to learn user behavior and optimize the future microblog application designs.

• Periodical: Advanced Materials Research (Volumes 926-930)
• Pages: 2781-2785
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.926-930.2781
• Online since: May 2014
• Authors: Song Zhang*, Jun Liu, Zhen Ming Lei, Wen Hui Lin, Qing Yan
• Keywords: MapReduce, Massive Data, Microblog, Mobile Internet, Traffic Volume

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