Small-time scaling behavior of Internet backbone traffic

Abstract

We perform an extensive wavelet analysis of Internet backbone traffic traces to observe and understand the causes of small-time scaling phenomena present in them. We observe that for a majority of the traces, the second-order scaling exponents at small time scales (1–100 ms) 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 (≈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 byte contribution and flow density. Our study points to the dense flows (i.e., flows with 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 influenced by traffic fluctuations at small time-scales, our observations and results have important implications for networking modeling, service provisioning and traffic engineering.