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The original GSS algorithm [8] is designed for scheduling retrieval requests in a magnetic disk. The traditional First-In-First-Out scheduling algorithm has poor disk utilization in continuousmedia applications because in the worst case the disk arm may need to seek back and forth between the innermost track and the outermost track, thus wasting a lot of time in seeking (cf. 3). Instead, some systems use the SCAN scheduling algorithm to reduce seektime overhead by serving requests while the disk arm scans across the disk surface. However, this approach requires two buffers per stream because requests may be served out of order and in the worst case, two requests for the same stream may be served in a back-to-back manner. The GSS algorithm is designed to strike balance between minimizing seek-time overhead and minimizing buffer requirement by serving streams in groups. Streams within a group are served using SCAN to reduce seek-time overhead while the groups are served in a xed order to

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State-variable initial values are simply additional parameters Each call of the DESIRE library function ran() produces a new sample of a pseudorandomnoise sequence Pseudorandom noise is really not random but a programmed number sequence that repeats after a large number of samples ran() output is uniformly distributed between 1 and 1 with theoretical mean 0 and variance 1/3 Different samples are uncorrelated but not statistically independent; this problem will be discussed in Section 5-4 The experiment-protocol command seed m can start or restart the noise sequence with a specific fixed value This can be useful for testing programs Various functions of the uniformly distributed ran() output can produce samples with different known probability distributions (see the references to 5) Sums y = ran() + ran() + with 4 7 terms are approximately Gaussian with mean 0 and variance N/3, where N is the number of terms But multiple samples of y are not necessarily jointly Gaussian MONTE CARLO SIMULATION OF DYNAMIC SYSTEMS 4-6 Repeated-run Monte Carlo Simulation

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reduce buffer requirement. By varying the number of groups, one can trade-off disk utilization against buffer requirement. To extend GSS for use in parallel video servers, we propose dividing a service round into G = g N S groups, where g can be determined using the single-server model [8] to minimize buffer requirement while still meeting the playout requirement. Assume that a single server can serve at most video sessions, then each group serves up to ( /g) video sessions. It is easy to see that this holds for two or more servers as well. Therefore, the number of disk buffers needed is reduced from N S to , though we still need N S network buffers because a video block is transmitted at a lower data rate of RV /N S . Under this extended GSS algorithm, the total amount of server buffer required will be Bserver = Q N S 1+ 1 G (10.24)

Repeated-run Monte Carlo simulation programs loop to exercise a DYNAMIC program segment n times with new random inputs and then take statistics on the results

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