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the scalability of the parallel video server architecture to a maximum of 408 servers serving a total of 3,672 concurrent video sessions at 90% utilization. If 1GB memory is available, the architecture can be scaled up to 14,400 concurrent video sessions using a client server ratio of 250 at 90% utilization. A second, more subtle limiting factor is due to the sub-schedule striping scheme. Under this scheme, the client must resequence the incoming data by copying U -bytes stripe units into the client buffer (Figure 10.6). Hence the processing overhead will likely increase with smaller striping size. Our previous experiences showed that processing overhead remains practical for software implementations running in even low-end PCs for striping size as small as 1KB. This limits N S to 64. For larger systems, we can use more powerful server hardware with a larger client server ratio to avoid reaching this limit. In the previous example with 1GB memory, we increase the client server ratio to 250 to limit to a total of 64 servers. Clearly the rapid improvement in CPU speed will undoubtedly extend this limit.

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(b) Interactive Monte Carlo Simulation: Computing Time Histories of Statistics with Compiled DOT Operations

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In this chapter, we have presented and analyzed a concurrent-push parallel video server architecture for designing scalable video-on-demand systems. The proposed architecture employs xed-size block striping and the server-push service model. To schedule disk retrievals and transmissions, we introduced a concurrent-push scheduling algorithm where video data are continuously transmitted from all servers to a client station. This constant-bit-rate traf c produced by the algorithm enables us to take advantage of the quality-of-service guarantees provided by the networks. To extend the scalability of the architecture, we introduced the Asynchronous Grouped Sweeping Scheme and the Sub-Schedule Striping Scheme into the architecture. Results showed that the resultant architecture can be scaled up to more than ten thousand concurrent users with acceptable buffer requirement and system response time. Building video-on-demand systems upon parallel server architecture not only breaks through the capacity limit of a single server, but also opens the way to fault-tolerant system designs.

Vectorized Monte Carlo simulation has another interesting and important feature Since a single simulation run samples all n replicated models at each point of time, one can compute and display time histories of statistics, and observe the results of parameter changes as the simulation run proceeds Such interactive Monte Carlo simulation was formerly possible only with very fast (and very inaccurate) analog computers [7] Runtime statistics computations are needed only at output-sampling times, not at every derivative call We will thus save time by programming DYNAMIC-segment statistics computations following an OUT or SAMPLE m statement (Section 1-6) As noted in Section 4-6a, most statistics are functions of sample averages A vectorized DYNAMIC program segment computes the sample averages

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