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Why You Need an Authorization Policy What to Include in an Authorization Policy Sample Authorization Policy Statements

gje x Pjj tjx dx;

Actually, Java is capable of using either slashes or backslashes depending on the host's file system.

0 c gje x Gjc tjx Gje t ;

if Gj has in nite variance. (As can be seen using integration by parts, the integral is the mean of Gje ; see Feller [18, p. 150]. In general, Gje has kth moment mk 1 Gj = k 1 m1 Gj , where mk Gj is the kth moment of Gj .) j Note that if approximation (17.58) holds, then the level process contributes to long-range dependence, but the within-level variation process does not, because

CHAPTER SECTIONS: 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 / 9

228 228 229

c Gje t Cov Wj 0 ; Wn t dt %

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We have shown how transient analysis to support network design and control can be carried out for both on=off and multilevel source traf c models with general, possibly heavy-tailed, level-holding-time distributions. In Section 17.4 we analyzed the transient behavior of a general source traf c model composed of a semi-Markov level process and a zero-mean piecewise-stationary within-level variation process. We approximated the conditional aggregate demand from many sources given system state information by the conditional aggregate mean given level values and ages. The within-level variation process plays no role in this approximation. We showed that the conditional mean can be effectively computed using numerical transform inversion and developed several approximations to it (Sections 17.5 and 17.8). We showed how the model can be exploited to study the value of information (Section 17.6). We applied our techniques to examples in network design (Section 17.8). Even though our approach is to focus on offered load, unaltered by loss and delay associated with nite capacity, we can apply the conditional mean approximation in Section 17.4 to develop an approximation to describe loss and delay from a nitecapacity system, just as described in Duf eld and Whitt [14, Section 5] for the M =G=I arrival process. Finally, our approach can be extended to a nonstationary setting in which connections arrive according to a nonhomogeneous Poisson process; see Duf eld et al. [16]. Then each active connection may generate traf c according to the model considered here. It is signi cant that it is possible to obtain computationally tractable descriptions of the time-dependent aggregate demand.

Unlike the technically adept attacks to be revealed in 5, these malicious applets are very easy to write There are malicious applets that play background sounds endlessly There are malicious applets that consume system resources, implementing denial-of-service attacks There are applets that forge electronic mail There are even applets that kill other applets' threads Now that techniques are widely available on the Hostile Applets Home Page (among other places), it is only a matter of time before malicious applets spread Because malicious applet source code has been put on the Web, hundreds of people can start to use and adapt the ideas We have been lucky that this has not happened yet Perhaps we will continue to be lucky, or perhaps not As we have seen, an applet need not break into your machine in order to do malicious things.

1. J. Abate, G. L. Choudhury, and W. Whitt. An introduction to numerical transform inversion and its application to probability models. In W. Grassman, ed., Computational Probability. Kluwer, Boston, pp. 257 323, 1999. 2. J. Abate and W. Whitt. Numerical inversion of Laplace transforms of probability distributions. ORSA J. Comput., 7:36 43, 1995.

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3 J Abate and W Whitt Modeling service-time distributions with non-exponential tails: beta mixtures of exponentials Stoch Models, 15:517 546, 1999 4 J Abate and W Whitt In nite series representations of Laplace transforms of probability density functions for numerical inversion J Opns Res Soc Japan 42, 1999, in press 5 J Abate and W Whitt Computing Laplace transforms for numerical inversion via continued fractions INFORMS J Comput, 11, 1999, in press 6 S Asmussen, O Nerman, and M Olsson Fitting phase-type distributions via the EM algorithm Scand J Statist 23:419 441, 1996 7 R Caceres, P G Danzig, S Jamin, and D J Mitzel Characteristics of wide-area TCP=IP conversations Comput Commun Rev, 21:101 112, 1991 8 H Chen and A Mandelbaum Discrete ow networks: bottleneck analysis and uid approximations Math Oper Res, 16:408 446, 1991 9 E Cinlar Introduction to Stochastic Processes.

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