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Small-Time Scaling Behavior of Internet Backbone Traffic

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Title: Small-Time Scaling Behavior of Internet Backbone Traffic
Author: Ribeiro, Vinay Joseph; Zhang, Zhi-Li; Moon, Sue; Diot, Christophe
Type: Journal article
Keywords: networks; long range dependence; scaling; short time scales; backbone
Publisher: Elsevier
Citation: V. J. Ribeiro, Z. Zhang, S. Moon and C. Diot, "Small-Time Scaling Behavior of Internet Backbone Traffic," Computer Networks, vol. 48, no. 3, pp. 315-334, 2005.
Abstract: We perform an extensive wavelet analysis of Internet backbone traffic signals to observe and understand the causes of small-time (sub-seconds) scaling phenomena present in them. We observe that for a majority of the traffic traces, the (second-order) scaling exponents at small time scales (1ms - 100ms) are fairly close to 0.5, indicating that traffic fluctuations at these time scales are (nearly) uncorrelated. Some traces, however, do exhibit moderately large scaling exponents (approximately 0.7) at small time scales. In addition, the traces manifest mostly monofractal behaviors at small time scales. To identify the network causes of the observed scaling behavior, we analyze the flow composition of the traffic along two dimensions -- flow size and flow density. Our study points to the dense flows (i.e., flows with bursts of densely clustered packets) as the correlation-causing factor in small time scales, and reveals that the traffic composition in terms of proportions of dense vs. sparse flows plays a major role in influencing the small-time scalings of aggregate traffic. Since queuing inside routers is strongly influenced by traffic fluctuations at small time-scales, our observations and results have significant implications in networking modeling, service provisioning and traffic engineering.
Date Published: 2005-06-01

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  • ECE Publications [1048 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.