Abstract
A goal of modern broadband networks is their ability to provide stringent QoS guarantees to different classes of users. This feature is often related to events with a small probability of occurring, but with severe consequences when they occur.
In this paper we focus on the overflow probability estimation and analyze the performance of Bridge Monte-Carlo (BMC), an alternative to Importance Sampling (IS), for the Monte-Carlo estimation of rare events with Gaussian processes.
After a short description of BMC estimator, we prove that the proposed approach has clear advantages over the widespread single-twist IS in terms of variance reduction. Finally, to better highlight the theoretical results, we present some simulation outcomes for a single server queue fed by fraction Brownian motion, the canonical model in the framework of long range dependent traffic.
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Giordano, S., Gubinelli, M., Pagano, M. (2007). Rare Events of Gaussian Processes: A Performance Comparison Between Bridge Monte-Carlo and Importance Sampling. In: Koucheryavy, Y., Harju, J., Sayenko, A. (eds) Next Generation Teletraffic and Wired/Wireless Advanced Networking. NEW2AN 2007. Lecture Notes in Computer Science, vol 4712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74833-5_23
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DOI: https://doi.org/10.1007/978-3-540-74833-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74832-8
Online ISBN: 978-3-540-74833-5
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