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-- VECTORIZED MONTE CARLO STUDY OF A RANDOM WALK ---------------------------------------------------------------------------------------------------------------irule 2 | -Euler integration NN = 10001 | TMAX = 1 DT = TMAX/NN | -< COMINT = TMAX/(NN- 1) a = sqrt(3 * NN) | ---scaled noise amplitude VAR0 = TMAX^2 | scale = VAR0 -n = 5000 | STATE x[n] | ARRAY noise[n] for i = 1 to n | noise[i] = a * ran() | next | -initialize drun write "type go to continue" | STOP ----------------------------------------------------------------------------------------------------------------post-run probability-density estimation ARRAY f[n] irule 0 | -just sampled data scale = 4 | TMAX = scale | NN = 2500 a = 2 * scale | b= - scale | -for display sweep t = 0 | h = 015 | -h is the Parzen-window width alpha = 1/(2 * h * h) | beta = 1/(h * n * sqrt(2 * PI)) drun PARZEN ---------------------------------------------------------------------------------------------------------------DYNAMIC ---------------------------------------------------------------------------------------------------------------Vectr d/dt x = noise OUT Vector noise = a * ran() -----------------------------------compute statistics DOT xSum = x * 1 | DOT xxSum = x * x xAvg = xSum/n | xxAvg = xxSum/n | xVar = xxAvg - xAvg^2 Var = t * DT * (a^2)/3 | -theoretical variance of x -xAvgx20 = 20 * xAvg | -scaled display dispt xVar, Var, xAvgx20 ---------------------------------------------------------------------------------------------------------------label PARZEN -xx = a * t + b | -display sweep for Parzen Window Vector f = beta * exp(- alpha * (xx - x)^2) DOT F = f * 1 | F = 10 * F - scale -- display Gaussian density for comparison yy = 10*exp(- (xx^2)/(2 * VAR0))/sqrt(2 * VAR0 * PI) - scale errorx2 = 2*(F - yy) + 05*scale | -deviation from normal density dispxy xx, yy, F, errorx2 | -scaled and offset display FIGURE 5-3 DESIRE program for the vectorized random-walk simulation and runtime statistics computations An extra DYNAMIC program segment estimates the post-run probability density and compares it with the Gaussian probability density.



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One of the desirable features of parallel server architectures is the incremental scalability, i.e., we can progressively add more servers to increase the system streaming capacity. In practice, a service operator will likely begin with a smaller system, and then gradually add more servers when the user population grows. This system expansion, however, creates two new challenges. First, as the media data are already stored among the existing media servers, we will need to redistribute some of the media data to the newly added servers so that they can share the streaming workload. Second, for fault tolerance, in addition to the media data, there will also be redundant data encoded from the media data using erasure correction codes. After adding more servers and redistributing the media data, however, the original redundant data unfortunately will become invalid as the size of the parity group has increased. Hence, we will need to update the redundant data so that the system s faulttolerant capability can be maintained. This chapter presents new algorithms to solve these two challenges.





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There are two challenges in expanding a parallel streaming server with more servers. First, the newly added servers cannot share the streaming workload until a portion of the media data have been redistributed to them from the existing media servers. We call this process data reorganization. While simple algorithms can easily be devised, our results show that they are very inef cient and incur signi cant overheads in transferring data between the existing and the new servers. To tackle this problem we present in Section 15.3 a Row-Permutated Data Reorganization (RPDR) algorithm that can ef ciently reorganize data in a parallel streaming server. Compared to the trivial algorithm, RPDR can reduce the reorganization overhead by over 70%. More importantly, RPDR can guarantee streaming load balance after the data have been reorganized. Second, if the parallel streaming server employs redundant data to support fault tolerance, then the redundant data will also need to be updated after the media data are reorganized. We call this the redundant data update problem. In Section 15.4 we present a Sequential

The sample-hold state variable noise and the extra differential-equation state variable W can be safely initialized with zero values The vectorized model in Figure 4-4 can be similarly amended

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