YouTube is the most popular service in today's Internet. Google relies on its massive Content Delivery Network (CDN) to push YouTube videos as close as possible to the endusers, both to improve their watching experience as well as to reduce the load on the core of the network, using dynamic server selection strategies. However, we show that such a dynamic approach can actually have negative effects on the enduser Quality of Experience (QoE). Through the comprehensive analysis of one month of YouTube flow traces collected at the network of a large European ISP, we report a real case study in which YouTube QoE-relevant degradation affecting a large number of users occurs as a result of Google's server selection strategies. We present an iterative and structured process to detect, characterize, and diagnose QoE-relevant anomalies in CDN distributed services such as YouTube. The overall process uses statistical analysis methodologies to unveil the root causes behind automatically detected problems linked to the dynamics of CDNs' server selection strategies.

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WhatsApp, the new giant in instant multimedia messaging in mobile networks is rapidly increasing its popularity, taking over the traditional SMS/MMS messaging. In this paper we present the first large-scale characterization of WhatsApp, useful among others to ISPs willing to understand the impacts of this and similar applications on their networks. Through the combined analysis of passive measurements at the core of a national mobile network, worldwide geo-distributed active measurements, and tra c analysis at end devices, we show that: (i) the WhatsApp hosting architecture is highly centralized and exclusively located in the US; (ii) video sharing covers almost 40% of the total WhatsApp tra c volume; (iii) flow characteristics depend on the OS of the end device; (iv) despite the big latencies to US servers, download throughputs are as high as 1.5 Mbps; (v) users react immediately and negatively to service outages through social networks feedbacks. 1

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