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The popularity of IEEE 802.11 WLANs has led to dense deployments in urban areas. High density leads to sub-optimal performance unless the interfering networks learn how to optimally use and share the spectrum. This paper proposes two fully distributed algorithms that allow (i) multiple interfering 802.11 access points to select their operating frequency in(More)
Caching is a key component for Content Distribution Networks and new Information-Centric Network architectures. In this paper, we address performance issues of caching networks running the RND replacement policy. We first prove that when the popularity distribution follows a general power-law with decay exponent α > 1, the miss probability is(More)
—The Internet heavily relies on Content Distribution Networks and transparent caches to cope with the ever-increasing traffic demand of users. Content, however, is essentially versatile: once published at a given time, its popularity vanishes over time. All requests for a given document are then concentrated between the publishing time and an effective(More)
The growth of User-Generated Content (UGC) traffic makes the understanding of its nature a priority for network operators, content providers and equipment suppliers. In this paper, we study a four-month dataset that logs all video requests to DailyMotion made by a fixed subset of users. We were able to infer user sessions from raw data, to propose a(More)
We evaluate the algorithm proposed in [1], which estimates the residual bandwidth on each hop of an Internet path using a para-metric model which consists of a Kelly queueing network. The evaluation is driven by simulation based on real network traces over a two node path. Correction factors are proposed and evaluated to cope with deviations from model(More)
The Internet increasingly focuses on content, as exemplified by the now popular Information Centric Networking paradigm. This means, in particular, that estimating content popularities becomes essential to manage and distribute content pieces efficiently. In this paper, we show how to properly estimate content popularities from a traffic trace.(More)