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1. P. Abry and D. Veitch. Wavelet analysis of long-range dependent traf c. IEEE Trans. Inf. Theory, 44:2 15, 1998. 2. P. Abry and D. Veitch. Wavelet for the analysis, estimation and synthesis of scaling data. In K. Park and W. Willinger, eds., Self-Similar Network Traf c and Performance Evaluation. Wiley, New York, 2000. 3. P. Barford and M. E. Crovella. Generating representative web workloads for network and server performance evaluation. In Proceedings of Performance'98=ACM SIGMETRICS '98, pp. 151 160, 1998. 4. P. Barford and M. E. Crovella. A performance evaluation of hyper text transfer protocols. In Proceedings of Performance'99=ACM SIGMETRICS'99, 1999. 5. J. Beran. Statistics for Long-Memory Processes. Chapman and Hall, New York, 1994. 6. V. A. Bolotin. Modeling call holding time distributions for CCS network design and performance analysis. IEEE J. Select. Areas Commun., 12(3):433 438, 1994. 7. M. E. Crovella and A. Bestavros. Explaining World Wide Web traf c self-similarity. Technical Report BU-CS-95-015, Boston University, Boston, 1995. 8. M. E. Crovella and A. Bestavros. Self-similarity in World Wide Web traf c evidence and possible causes. IEEE=ACM Trans. Networking, 5(6):835 846, 1997. 9. D. R. Cox. Long-range dependence: a review. In H. A. David and H. T. David, eds., Statistics: An Appraisal, pp. 55 74. Iowa State University Press, Ames, 1984. 10. I. Daubechies. Ten Lectures on Wavelets. SIAM, Philadelphia, 1992. 11. S. Deng. Empirical model of WWW document arrivals at access link. In Proceedings of ICC=SUPERCOMM'96, pp. 1197 1802, 1996. 12. D. E. Duffy, A. A. McIntosh, M. Rosenstein, and W. Willinger. Statistical analysis of



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The key term in MTBF is mean time A mean time between failures number is just that, a mean An average If a disk drive has an MTBF of 200,000 hours (almost 23 years), that does not mean that every disk rolling off the assembly line is guaranteed to work for exactly 23 years, and then drop dead When the government announces life expectancy figures, it certainly doesn t mean that every person in America will die when he reaches the age of 769 years For every person who doesn t make it to his 50th birthday, there is going to be someone whose picture makes it onto The Today Show for surpassing her 100th birthday Means are trends.

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If you look at all the disks that roll off the assembly line during a given period of time, the average life expectancy of a disk, before it fails, is about 23 years That means, however, that some disks may fail the first day and others may last 40 years (obsolescence aside) It also means that if you have a large server, with, say, 500 disks in it, on average you will lose a disk every 200,000/500 or 400 hours Four hundred hours is only about 161 2 days So, you will be replacing a disk, on average, every 21 2 weeks A statistical mean is not enough to tell you very much about the particular members of a population The mean of 8, 9, 10, 11, and 12 is 10 But the mean of 1, 1, 1, 1, and 46 is also 10.

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So is the mean of 12,345, -12,345, 47,000,000, -47,000,000, and 50 The other key number is standard deviation, or sigma ( ) Without going into a long, dull explanation of standard deviation calculations (go back and look at your college statistics textbook!), tells you how far the members of a population stray from the mean For each of the three previous examples (and treating each like a complete population, for you statistics nerds), is as.

Currently, the vast proportion of innovation is happening on the buy side of consumer interactions. The sell side innovations are progressively being less and less. Within 10 years, consumers will be the sole integration point of their own information, and businesses will be operating in the complete reciprocal of their current customer management strategies and practices. This consumer information shift will have the historic and economic signi cance of the Industrial Revolution. The seemingly fundamental transformational effects that the advent of Google, YouTube, eBay, and Facebook has had on the evolution from a centralized and structured system of business to one of conversation and collaboration between individuals will pale in comparison to a world in which the consumer [is] the point of information integration. A world where consumers initiate each consumption activity/interaction, manage their own experience, and determine what information is collected, how it is used, and how it is disseminated. The current-day equivalent would be our B2B business model relative to RFI s [Request For Information] except they will be issued by individuals to businesses, i.e., Personal RFI s. This business model will spawn an entire new industry for the information machine required to match the individual s request and potential suppliers or 3rd or 4th party service providers, i.e., information intermediaries. It will also give rise to an entire new system of protecting information privacy for managing this new information ow in both directions.

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