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exponential model, the exponential model can sometimes result in a better t than the Weibull model. This is possible since the parameter estimation for the Weibull model is more complex and can be less precise than for the exponential model.) Similar observations apply to the CMU datasets and other applications. Given that the Weibull model is superior for many applications and that its shape parameter c may vary quite substantially from one time period to the next, it is of interest to look at the distribution of the shape parameter c of the tted Weibull distributions. Figure 15.13(b) gives the histogram of Weibull shape parameter c for the tted Weibull distribution of the external AT&T dataset from 16 to 31 March 1996 and Fig. 15.13(c) does the same for the tted Weibull distributions for the applications TELNET, X, FTP, HTTP, RFS, SMTP, and FINGER over two hour time periods for all CMU datasets. That the number of Weibull models that have a shape parameter around one is small is another indication that the exponential model is inappropriate to model these interarrival times. That more than 50% of the shape parameters are less than 0.65 indicates the inherent burstiness of the request sequences.



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That s not exactly right. More accurate would be: We can already read your mind. If you re like me, you were fascinated by Martin Lindstrom s book Buyology: Truth and Lies About What We Buy (Doubleday, 2008). He spent four years putting willing subjects into Functional Magnetic Resonance Imaging systems and watching which parts of their brains lit up when shown a variety of print ads, TV shows, and brand images. Wonderful stuff. What follows, however, is the ultimate in social media metrics because it s the ultimate in human metrics. It is literally reading your mind over the Internet just by your visiting any web site that uses technology and analysis from NextStage Evolution (www.nextstagevolution.com). You are now experiencing one of two reactions and likely both at the same time. 1. Jim has well and truly lost it. 2. This should be interesting. Stick with me; I think you ll nd #2 to be dominant by the end.

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To underscore the observation that the Weibull distribution gives good ts especially for HTTP TCP connection interarrival times, we again consider traces from AT&T's WorldNet. The rst trace was collected on the 23 July 1997 starting at 7 p.m. for a duration of seven hours on an FDDI ring carrying traf c from roughly 420 modems used by WorldNet dialup customers (for more details see Feldmann et al. [13, 15]). For this dataset we consider the interarrival time distribution of HTTP connections over all 36 possible 12 minute time periods. In all instances the Weibull models are statistically better than the Pareto and exponential models, and better than the lognormal in 91.6% of the case. Never is any other model statistically better than the Weibull one. Similar observations apply to other subsets. One of the problems in this dataset is that it includes 371,972 HTTP connections. With this many connections arriving in a bursty fashion the timer resolution can become a potential problem. This can disturb small interarrival times and therefore undersample them. Figure 15.14(a) shows the histogram of the interarrival times for a two hour sample of the data. The spikes in the range of 0.0001 0.001 second have two origins: timer inaccuracy and browsers almost simultaneously opening multiple TCP connections. Nevertheless, the data, except for the spikes, is still well modeled by a Weibull distribution. Figure 15.14(b) shows the histogram of Weibull shape parameter c for the tted Weibull distributions. The backbone AT&T WorldNet dataset contains so many HTTP requests (1,187,865 over one hour) that the timer resolution on the monitoring machine is not suf cient to resolve the small time scale effects. Therefore we do not attempt to model the interarrival times for this dataset.

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In the following we summarize related work in the area of using heavy-tailed distributions in traf c modeling.

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