By Uwe Bauknecht, Frank Feller (auth.), Thomas Bauschert (eds.)
This e-book constitutes the refereed lawsuits of the nineteenth EUNICE/IFIP WG 6.2, 6.6 workshop on Advances in verbal exchange Networking, EUNICE 2013, held in Chemnitz, Germany, in August 2013.
The 23 oral papers established including nine poster displays have been rigorously reviewed and chosen from forty submissions. The papers are prepared in topical sections on community modeling and layout, site visitors research, community and site visitors administration, providers over cellular networks, tracking and dimension, safety innovations, software of ICT in shrewdpermanent grid and clever domestic environments, facts dissemination in ad-hoc and sensor networks, and prone and applications.
Read Online or Download Advances in Communication Networking: 19th EUNICE/IFIP WG 6.6 International Workshop, Chemnitz, Germany, August 28-30, 2013. Proceedings PDF
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Additional resources for Advances in Communication Networking: 19th EUNICE/IFIP WG 6.6 International Workshop, Chemnitz, Germany, August 28-30, 2013. Proceedings
However, current literature lacks in proﬁling users in such regards. In this paper we investigate the elephant and mice phenomena regarding Internet users. We analyzed two recent measurements taken from high speed operational networks and found that elephant users show similar packet level characteristics to elephant ﬂows. We also determined that there is a much smaller overlap between these two phenomena that one would expect. We found that only a small portion (10%-30%) of elephant ﬂows are generated by elephant users and also the generation of elephant ﬂows is not a necessary condition for being an elephant user.
K), which can be expressed as follows: K eQτ = Qi i=0 τi . i! (5) The other common alternative methods to obtain the transient probability vector π(τ ) include uniformization , matrix exponential approach . Here we take uniformization approach as the model analysis example. Let qii be the Survivability Modeling under Fault Propagation 33 diagnoal element of Q and I be the unit matrix, then the transient state probability vector is obtained as follows: ∞ π(τ ) = π(0) e−βτ i=0 (βτ )i i P, i!
For example, in network deployment in coastal areas, which are vulnerable to speciﬁc disaster types like ﬂooding as well as hurricanes, the knowledge of network survivability is useful. The rest of the paper is organized as follows. In Section 2, we develop Markov models for a system from the survivability quantiﬁcaiton view point. Section 3 analyzes the models that may be used to ﬁnd the transient probabilities leading to the computation of transient survivability measures. Numerical results of the analysis performed on the models are presented in Section 4.