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Let CondProbt, CondProbtH be two instances of the conditional probability density table measured at time instances t > t H at least T1 T2 apart Then for each L1 condition l P 1; 8 , SAC computes kCondProbt l CondProbtH l k2 < Y; and if the check passes, allows this particular conditional probability density to be used in the actuation part of SAC when updating the data rate (see Section 18422) Here, Y > 0 is an accuracy parameter and k k2 is the ``L2 '' norm (not to be confused with the random variable L2 ) Figure 1810 depicts the convergence rate of three conditional probability densities conditioned on l 1; 2, and 8 by plotting the distance measure computed above Figure 1810(b) l 2 and Fig 18.

import java.lang.System; import java.io.IOException; import java.lang.Math; class Ackerman { public static void main(String[] args) { long ackValue = 1; long exp = 0; if (args.length >= 1) { try { exp = Integer.parseInt(args[0]); } catch (NumberFormatException e) { exp = 0; } } if (exp > 1) { ackValue = exp; int numLoops = (int)exp; for (int i = 1; i < numLoops; i++) { exp = ackValue; for (int j = 1; j < numLoops; j++) { ackValue = ackValue * exp; System.out.println("current value is " + ackValue); } } System.out.println("Ackerman vlue: " + ackValue); } }

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Getting to more specificity with guiding principles benefits the architecture team by eliminating guesswork and providing general parameters for the team s decision making. Interviewing people in the organization chain of command all the way to top management is one way to achieve additional valuable insight to what thoughts or ideas are currently guiding or directing the organization. The same is true of government entities. A change in governors or legislative committees can have significant impact on the direction an architecture team could take. For example, the previous department director or governor may have favored outsourcing whenever possible, and the current leadership might prefer developing internal talent and doing things in-house. Nonprofits also change leaderships or boards of directors, and a change in toplevel governance likely will lead to at least a different prioritization if not a complete shift in direction for the organization.

10(c) l 8 are the typical plots whose convergence rate is followed by the ones in the range 2 < l < 8 as well Figure 1810(a) l 1 , however, is atypical and only holds for condition L1 1 This mainly stems from the fact that for condition L1 1, very few sample points <100 arise even during a 10,000 s run, and, as a result, the estimated probabilities are volatile This, in turn, can be attributed to the fact that we are computing statistics for a long-range dependent process, and it is well known that for such processes a very large number of samples are needed to compute even its rst-order statistics accurately A related discussion can be found in Crovella and Lipsky [9] The fact that this volatility stems from too few sample points also delimits the impact of this phenomenon.

Namely, by Amdahl's law, the instances (over time) where the conditional probability PrfL2 jL1 1g may have been invoked are so few to begin with that the loss from not having taken any actions at those instances is negligible with respect to overall performance 1853 18531 Performance Measurement of SAC Incremental Gain of Selective Aggressiveness.

This denial-of-service approach is simple and elegant. There are literally hundreds of things that can be done in Step 5. Other possibilities include endlessly appending to a StringBuffer and using drawString to display its entire contents. This ends up as a double whammy, eating both CPU cycles and memory. Another possibility would be calculating p using the most inefficient algorithm possible. If you have trouble remembering how to code poorly, just teach an introductory programming course to jog your memory.

Unimodal Throughput Curve In this section, we evaluate the relative performance of SAC and its predictability gain. We measure the incremental bene t gained by

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Fig. 18.10 Convergence of conditional probability densities for a 1:05 traf c: (a) l 1, (b) l 2, and (c) l 8.

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