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Inferring the Periodicity in Large-Scale Internet Measurements

Many Internet events exhibit periodical patterns. Such events include the availability of end-hosts, usage of internetwork links for balancing load and cost of transit, traffic shaping during peak hours, etc. Internet monitoring systems that collect huge amount of data can leverage periodicity information for improving resource utilization. However, automatic periodicity inference is a non trivial task, especially when facing measurement “noise”.

      In this paper we present two methods for assessing the periodicity of network events and inferring their periodical patterns. The first method uses Power Spectral Density for inferring a single dominant period that exists in a signal representing the sampling process. This method is highly robust to noise, but is most useful for single-period processes. Thus, we present a novel method for detecting multiple periods that comprise a single process, using iterative relaxation of the time-domain autocorrelation function. We evaluate these methods using extensive simulations, and show their applicability on real Internet measurements of end-host availability and IP address alternations.

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