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If you find a larger operation daunting, and most people do at some point, the most mature thing you can do is to set your pride aside and broker the business while you are holding the aces You don t have to sell it all; you can retain that slice of the action where you truly excel You will do much better for yourself in the long run being best at what you can do than scraping by at what you can t Whatever you decide, it is an important decision My advice is take a vacation Recharge your batteries Listen to those you hold most dear Consult your inner voice Take your time; but not too much time Competitors are bound to have noted your success They will be looking for your program s limitations.

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performance robustness quanti es how rapidly a desired network performance metric changes with respect to changes in a disturbance As presented by Yin et al [13], the idea is to de ne robustness based on how network performance differs from its reference point in the presence of a disturbance, as depicted in Figure 63 In practice, the network performance reference point can be in the form of a performance target for which the system is being designed, and usually but necessarily, the one when there is no disturbance Let the performance reference be a random variable with a cumulative distribution function F Let the network performance observed in the presence of disturbance be a random variable with a cumulative distribution function G Then we need to quantify the difference between G and F Logically, the smaller the difference, the better the performance robustness.

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Thus, for the purpose of de ning a quantitative metric of robustness, we need to measure the distance or difference between the two distribution functions properly How can we de ne the distance between two distribution functions so that it is suitable to measure the performance robustness A simple way is to adopt one of the existing distributional testing statistics, such as the Kolmogorov Smirnov test statistic, 2 test statistic, Student-t test statistic, or even the Kullback Leibler distance However, we nd that none of these statistics is suitable to describe robustness for communication network performance We do not choose 2 and Student-t tests because both are based on Gaussian normal distributions It is not necessary to assume that the distribution functions involved are normal In [14], a robustness metric has been proposed based on the Kolmogorov Smirnov statistic.

23-12.VBS do set index = CreateObject( ixsso.Query ) index.MaxRecords = 20 index.SortBy = rank[d] index.AllowEnumeration = true index.Catalog = system index.Columns = filename, size, hitcount, rank, path, _ & characterization criteria = InputBox( Search for: ) if criteria=vbEmpty then exit do index.Query = criteria set rs = index.CreateRecordset( nonsequential ) display file names list = do until rs.EOF list = list & rs( path ) & vbCr rs.MoveNext loop MsgBox list if index.QueryTimedOut then MsgBox Your query was too complex! end if if index.QueryIncomplete then MsgBox Search result is not complete! end if if index.OutOfDate then MsgBox Index is not up-to-date. Results may not _ & be accurate. end if loop MsgBox Done!

u = r/ .

Similar to the Anderson Darling goodness-of- t test, the metric de ned by England et al [14] gives more weight to the tails than does.

that satisfy:

x 10

Listing 3.3 Document using attributes (withattribs.xml).

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