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Predicting Network Response Times Using Social Information

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4 Author(s)
Chen Liang ; Center for Secure Inf. Syst., George Mason Univ., Fairfax, VA, USA ; Sharath Hiremagalore ; Angelos Stavrou ; Huzefa Rangwala

Social networks and discussion boards have become a significant outlet where people communicate and express their opinion freely. Although the social networks themselves are usually well-provisioned, the participating users frequently point to external links to substantiate their discussions. Unfortunately, the sudden heavy traffic load imposed on the external, linked web sites causes them to become unresponsive leading to the "Flash Crowds" effect. In this paper, we quantify the prevalence of flash crowd events for a popular social discussion board (Digg). We measured the response times of 1289 unique popular websites. We were able to verify that 89% of the popular URLs suffered variations in their response times. By analyzing the content and structure of the social discussions, we were able to forecast accurately for 86% of the popular web sites within 5 minutes of their submission and 95% of the sites when more (5 hours) of social content became available. Our work indicates that we can effectively leverage social activity to forecast network events that will be otherwise infeasible to anticipate.

Published in:

Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on

Date of Conference:

25-27 July 2011