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.
Keywords
- Time Series
- Markovian Arrival Process
- Simple Network Management Protocol
- Bandwidth Estimation
- Link Usage
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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de O. Schmidt, R., Sperotto, A., Sadre, R., Pras, A. (2012). Towards Bandwidth Estimation Using Flow-Level Measurements. In: Sadre, R., Novotný, J., Čeleda, P., Waldburger, M., Stiller, B. (eds) Dependable Networks and Services. AIMS 2012. Lecture Notes in Computer Science, vol 7279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30633-4_18
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DOI: https://doi.org/10.1007/978-3-642-30633-4_18
Publisher Name: Springer, Berlin, Heidelberg
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