On the feasibility of utilizing correlations between user populations for traffic inference

Kun Chan Lan, John Heidemann

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

Network models today are often derived from two different methods. On one hand, detailed traffic models are generated based on traces from a single tap into the network. Alternatively, one can collect higher-level traffic-matrix data with SNMP from many routers. However, inferring flow-level details from such data is still an open research issue. Today it is infeasible to collect a fine-grained, packet-level representation of a complete, multi-router network. Even if it were economically feasible to synchronize and monitor every router in a large network, the amount of data generated would tax storage and computation resources. In this work, we propose a methodology to infer flow-level traffic across a network by exploiting the correlations between user populations across different networks. The contribution of this paper is twofold. First, based on traces of web traffic collected from two different sources, we observe that the user-behavior parameters of the traffic (such as user "think" time in web traffic) are correlated across time, while the application-specific parameters of the traffic (such as object size) are correlated across "similar" networks. Second, by utilizing the correlations between similar networks, we propose a methodology for inferring traffic at places where continuously taking measurements is infeasible. We evaluate the effectiveness of our methodology via simulation.

原文English
主出版物標題Proceedings - The IEEE Conference on Local Computer Networks - 30th Anniversary, LCN 2005
頁面132-139
頁數8
DOIs
出版狀態Published - 2005
事件IEEE Conference on Local Computer Networks - 30th Anniversary, LCN 2005 - Sydney, Australia
持續時間: 2005 十一月 152005 十一月 17

出版系列

名字Proceedings - Conference on Local Computer Networks, LCN
2005

Other

OtherIEEE Conference on Local Computer Networks - 30th Anniversary, LCN 2005
國家/地區Australia
城市Sydney
期間05-11-1505-11-17

All Science Journal Classification (ASJC) codes

  • 工程 (全部)

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