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Towards Bandwidth Estimation Using Flow-Level Measurements

Abstract : Bandwidth estimation is one of the prerequisite for efficient link dimensioning. In the past, several approaches to bandwidth estimation have been proposed, ranging from rules-of-thumb providing over-provisioning guidelines to mathematically backed-up provisioning formulas. The limitation of such approaches, in our eyes, is that they largely rely on packet-based measurements, which are almost unfeasible considering nowadays load and speed (1–10 Gbps). In this context, flow-based measurements seems to be a suitable alternative, addressing both data aggregation as well as scalability issues. However, flows pose a challenge for bandwidth estimation, namely the coarser data granularity compared to packet-based approaches, which can lead to a lower precision in the estimation of the needed bandwidth. In this paper, we investigate what is the impact of flow-based measurements on bandwidth estimation. In particular, we are interested in quantifying the impact of flows on main statistical traffic characteristics, in particular the traffic rate variance. Our approach is validated on real traffic traces captured from 2002 to 2011 at the University of Twente.
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Ricardo De O. Schmidt, Anna Sperotto, Ramin Sadre, Aiko Pras. Towards Bandwidth Estimation Using Flow-Level Measurements. 6th International Conference on Autonomous Infrastructure (AIMS), Jun 2012, Luxembourg, Luxembourg. pp.127-138, ⟨10.1007/978-3-642-30633-4_18⟩. ⟨hal-01529783⟩



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