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In the open class interval (a, b), u(x) = 1 and is 0 elsewhere (see also Section 2-8b) We find the desired statistical relative frequency hh easily and quickly with DOT hh = u * 1 Statistical relative frequencies can be computed as post-run Monte Carlo statistics Vectorized Monte Carlo studies can, instead, compute statistical relative frequencies at each sampling point to produce their time histories For a given Monte Carlo sample (x[1], x[2], , x[n]), the sample range range = xmax xmin is the difference between the largest value xmax and the smallest value xmin in the sample The DYNAMIC program segment of a vectorized Monte Carlo study can compute xmax, xmin, and range at each point of time to produce their time histories With reference to Section 3-8, we declare an n-dimensional vector xx and add the DYNAMIC-segment lines

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function text_example() { return 'Why, this text was just output using the shortcode tag you created! Smashing.'; }

Then we have (10.48)

Vector xx^ = x Vector xx^ = - x | | DOT xmax = xx * 1 DOT mxmin = xx * 1

3. Now you need to add text_example() to a shortcode. You do that with the add_shortcode()

(10.49)

Finally, making use of the result that x/y y (x y) we can then obtain i,k, j = (di dk ) TF (di dk ) TF (10.50)

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The experiment-protocol script then computes range = xmax + mxmin For repeated-run Monte Carlo simulation, post-run computation of xmax and xmin requires a search loop in the experiment protocol As we already noted, many other statistics (correlation and regression coefficients, and test statistics such as t and 2)[5] are functions of sample averages Post-run estimation of probability densities will be discussed in the next section 4-9 Post-run Probability-density Estimation [8,9]

function:

Let the new session request arrive at the admission scheduler at time t during group vnew = t G/TF . Then due to clock jitter, the current group at other servers can range from (t )G/TF to (t + )G/TF . To guarantee that the assigned group has not started in any of the servers implies assigning a group larger than the largest current group in any of the servers, i.e. snew = (t + )G/TF + 1. Applying the inequality x + y x + y we have snew t G/TF + G/TF + 1 Substituting into vnew we have snew vnew + G/TF + 1 and the results follows. (10.51)

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(a) A Simple Probability-density Estimate For continuous random variables x the probability density x(X) for each value X of x is approximated by

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[1] T.C. Chiueh, C. Venkatramani, and M. Vernick, Design and Implementation of the Stony Brook Video Server, Technical Report TR-16, Computer Science Department, State University of New York at Stony Brook, August 1995. [2] F.A. Tobagi and J. Pang, StarWorks: A Video Applications Server, IEEE COMPCON Spring 93, 1993, pp. 4 11. [3] R. Buck, The Oracle Media Server for nCube Massively Parallel Systems, Proceedings of 8th International Parallel Processing Symposium, 1994, pp. 670 673. [4] H. Taylor, D. Chin, and S. Knight, The Magic Video-on-Demand Server and Real-Time Simulation System, IEEE Parallel and Distributed Technology: Systems and Applications, vol. 3, no. 2, 1995, pp. 40 51. [5] D.J. Gemmell, H.M. Vin, D.D. Kandlur, P.V. Rangan, and L.A. Rowe, Multimedia Storage Servers: A Tutorial, IEEE Computer, vol. 28, no. 5, May 1995, pp. 40 49. [6] D. Mills, Internet Time Synchronization: The Network Time Protocol, IEEE Transactions on Comm., vol. 39, no. 10, Oct. 1991, pp. 1482 1493. [7] Z. Yang and T.A. Marsland, (eds) Global States and Time in Distributed Systems, IEEE Computer Society Press, Los Alamitos, CA, USA, 1994. [8] P.S. Yu, M.S. Chen, and D.D. Kandlur, Grouped Sweeping Scheduling for DASD-based Multimedia Storage Management, ACM Multimedia Systems, vol. 1, 1993, pp. 99 109. [9] J.N. Lloyd and K.S. Samuel, Urn Models and their Application, John Wiley & Sons, 1997, pp. 125 126.

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