Social Web Mining for Addressing Flash Crowds in CDNs
نویسنده
چکیده
The paper presents a survey of the current replica placement strategies on surrogates in Content Delivery Networks and proposes novel ways in which mining social networks can help derive useful insights in predicting user traffic on websites thereby helping CDNs to prepare for the abnormal spurts and traffic outages (called Shashdot Effect or Flash crowds). In this paper, we present metrics to extract vital information from tweets that we later feed to Machine Learning algorithms which eventually help in taking important decisions on moving user content around the CDN composed of hundreds of surrogate servers scattered across the globe.
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