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More recently, Abry and Veitch [1, 2] proposed a wavelet-based technique for analyzing long-range dependent data and for estimating the associated Hurst parameter Yet, more important, their method allows the identi cation of scaling regions, breakpoints, and nonscaling behavior and yields an unbiased estimator for the Hurst parameter (see Feldmann et al [16] for examples) Abry and Veitch's method utilizes the ability of wavelets to ``localize'' a signal in both time and scale (see Kaiser [26] for an introduction to wavelets, and Daubechies [10] for a more mathematical treatment of the subject) Given a process X , the discrete wavelet transform of the process will result in a set of wavelet coef cients dj;k that capture the contribution of the process at scale j and time 2j k.



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If X is a self-similar process with Hurst parameter H P 1 ; 1 , then 2 Abry and Veitch [1] have shown that the expectation of the energy Ej that lies within a given bandwidth 2 j around frequency 2 j l0 is given by # 1P 2 jd j cj2 j l0 j1 2H ; E Ej E Nj k j;k where c is a prefactor that does not depend on j, and where Nj denotes the number of wavelet coef cients at scale j One can study the scaling of a process by plotting log2 Ej against scale j (where j 1 is the nest scale and j N > 1 is the coarsest) The scaling analysis of a signal that is exactly self-similar will yield a linear plot for all scales; for a Poisson trace (ie.





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While social media becomes more and more important from the standpoint of getting the word out, the impact of that word is still going to be measured by the number of people it drives to your web site, your store, and your wallet The vast amount of information already available about onsite behavior must be married up with the social data ow.

, H 0:5) the plot will be a horizontal line On the other hand, for an asymptotically self-similar signal a linear relationship between log2 Ej and scale j will be apparent only for the larger times or scales ".

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If we apply the wavelet technique to our datasets, we con rm our suspicion that the HTTP connection arrival process is asymptotically self-similar. Figure 15.2 shows the scaling plots for some of the busier two hour periods of the datasets. All plots show a clear nonhorizontal scaling region from about scale 4 6 to scale 12, verifying the assumption of self-similar nature of the arrival process. The corresponding Hurst ^ parameter estimation results, based on the larger scales, are all around H % 0:7. To estimate simple trends (e.g., linear and x2 trends) we used Daubechies wavelets [10] with three vanishing moments. To contrast these results with the analysis of Paxson and Floyd [35], Fig. 15.3 shows the results of the scaling analysis for two subsets of the DEC-PKT- 4 dataset. The rst dataset contains all FTP CONTROL connection arrivals while the second one contains all HTTP connection arrivals. In line with the analysis results reported by Paxson and Floyd [35], the arrival process of FTP CONTROL connections gives a Hurst

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Fig. 15.2 Scaling analysis of arrival process of HTTP connection arrival for subsets of our datasets. Note the different labeling at the bottom and the top of the plots: scale j (bottom, j 1 is nest scale); actual time (top, in seconds). The vertical bars at each scale represent 95% con dence intervals for log2 Ej .

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Fig. 15.3 Scaling analysis of arrival process of FTP CONTROL and HTTP connection arrival for dataset DEC-PKT-4.

Both surveys agree that human error is a major cause of downtime, although they disagree on the degree of downtime that it causes. People cause downtime for two closely related reasons. The first reason is that they sometimes make dumb or careless mistakes. The second reason is that they do not always completely understand the way a system operates. The best way to combat people-caused downtime is through education combined with simple system design. By sending your people to school to keep them up-to-date on current technologies, and by keeping good solid documentation on hand and up-to-date, you can reduce the amount of downtime they cause.

parameter estimation of 0.5, which is consistent with a Poisson process. Yet, even in this dataset, the process that counts the number of HTTP connections is consistent with a self-similar process with Hurst parameter of H 0X65. To underline the observations that the number of Web connection arrivals is consistent with asymptotic self-similar behavior, Fig. 15.4 shows the results of the scaling analysis for data collected on AT&T WorldNet's network. The rst dataset was collected on 14 August 1998 at 10:30 p.m. on a T3 backbone link while the second dataset was collected on 23 July 1997 at 7 p.m. on a FDDI ring carrying traf c from roughly 420 modems used by WorldNet dialup customers (for more

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