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We evaluate performance of the MDR scheduler and compare it to Optimal Smoothing [1] in this section. To obtain realistic performance results, we collected the video bit-rate traces of 274 different videos from DVD movies for simulation. These are full-length (average 5,781 seconds long and 4,348 MB in size), MPEG-2 encoded videos with an average bit-rate of 6.02 Mbps. The bit-rate varies from below 0.5 Mbps to over 18 Mbps. Long-range (minutes to tens of minutes) bit-rate variations are common in these real-world MPEG-2 encoded videos. We implemented the MDR scheduler presented in Section 7.3.1 in software and used it to compute the transmission schedule for the videos. We also implemented the Optimal Smoothing algorithm [1] for comparison purposes. The generated transmission schedules are then fed into a simulator developed using CNCL [16] to obtain simulation results. The simulation model consists of a system with clients connecting through a 1Gbps backbone network to a server storing the 274 VBR videos. We assume that the backbone network is the bottleneck of the system. For simplicity, we ignore delay and loss in the network. New stream requests are generated according to a Poisson process with various mean inter-arrival times to simulate different system utilization. A new stream request randomly selects a video from the 274-video collection with uniform probability. Note that we adopt the uniform popularity instead of the Zipf popularity model [17] because the video titles have varying bandwidth requirements and lengths. Consequently, using the Zipf popularity will result in large variations in the simulation results, depending on which of the video titles happened to be picked as the hot titles. To obtain more consistent results for comparison, we therefore adopt the uniform popularity model.

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Table 7.1 Comparison of admission complxity between the Optimal Smoothing and the MDR schedulers Unsuccessfull Admission # Scheduler Temporal smoothing complexity at = 0.6 complexity at = 0.7 complexity at = 0.8 complexity at = 0.9 MDR scheduler complexity at = 0.6 complexity at = 0.7 complexity at = 0.8 complexity at = 0.9 Comparisons 0.00 (0.00) 0.00 (0.00) 0.19 (0.17) 5.32 (4.89) 0.00 (0.00) 0.00 (0.00) 0.17 (0.17) 4.83 (4.83) Additions 0.00 (0.00) 0.00 (0.00) 0.19 (0.17) 5.32 (4.89) 0.00 (0.00) 0.00 (0.00) 0.17 (0.17) 4.83 (4.83) Successfull Admission Comparisons 5782.52 5782.20 5782.04 5782.08 1 1 1 1 Additions 5782.52 5782.20 5782.04 5782.08 5782.60 5782.28 5782.11 5782.16

Note that successive operations for different models are interleaved in the computer memory Scalar quantities are common to all the replicated models and must, therefore, not depend in any way on the replicated-model vectors That said, scalars can be defined by the experiment protocol, by DYNAMIC-segment assignments, or even by differential equations

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Notes: *Numerical results are measured as the average number of computations required to admit a client at a given average network utilization ( ), averaged over requests for all 274 videos. # Numbers in parentheses are the average number of unsuccessful admission tests performed for each request.

Neural-network simulations can replicate different neuron models ( 6) The Method of Lines represents suitable partial differential equations as sets of ordinary differential equations (Sections 7-10 to 7-14) Map-based agroecology simulations replicate models of crop growth or species competition at different points of a landscape (Sections 7-15 and 7-16) 3-3 Matrix-vector Products in Vector Expressions

To admit a new stream, network resource reservations are performed according to the generated transmission schedules on a per-stream basis. Admission test for schedules generated by optimal smoothing is performed with a round length of one second (cf. Section 7.3.2). Each simulation run simulates a duration of 3,000 days. We summarize the results in the following sections.

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Table 7.1 compares the admission complexity of Optimal Smoothing and the MDR scheduler. The simulation results are obtained from counting the average number of computations required to admit a new client. We separate the computations incurred in unsuccessful and successful admissions. For unsuccessful admissions, the computation complexity is comparable for the MDR scheduler and the Optimal Smoothing scheduler. By contrast, the MDR scheduler requires signi cantly fewer computations than the Optimal Smoothing scheduler for successful admissions, which dominates the total admission complexity.

(a) Definition Any n-dimensional vector in the vector expressions f or g in Sections 3-1 and 3-2, say y2, can be a matrix-vector product A * v Here, A is a rectangular n m matrix, and v is an m-dimensional vector,6 both declared in the experiment protocol Expressions for A or v cannot be substituted, but A and v can be

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