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Clearly, for any given solution S of H-SCHED (G, Mhetero ) it can be veri ed in polynomial time that S is feasible (Algorithm 5) and sl(S) T ; hence, H-SCHED (G, Mhetero ) NP For any instance of SCHED (GSCHED , PSCHED ) an instance of H-SCHED (G, Mhetero ) is constructed by simply setting G = GSCHED , P = PSCHED , = w, and T = TSCHED ; thus all processors of P are identical and the node execution time is only a function of the node n: (n, P) = w(n) P P Obviously, this construction is polynomial in the size of the instance of SCHED (GSCHED , PSCHED ) Furthermore, if and only if there is a schedule for the instance of H-SCHED (G, P) that meets the bound T , is there a schedule for the instance SCHED (GSCHED , PSCHED ) meeting the bound TSCHED 631 Scheduling As the modi cation carried out for heterogeneous systems only affects the nish time calculation of a node, virtually all scheduling heuristics designed for homogeneous systems can be used for heterogeneous systems However, in order to produce ef cient schedules, a scheduling heuristic should be aware of the heterogeneity of the processors Yet again, this can often be achieved with a simple generalization Instead of making decisions based on the start time of a node, the nish time naturally includes the processing capacity of the processor For example, the possible start time of a node n might be earlier on processor P than on processor Q, but n might nish earlier on Q, due to Q s faster execution So both the state of the current partial schedule and the heterogeneity of the processors are considered in decisions based on the nish time Finish Time Minimization With the above argumentation, the common start time minimization in list scheduling (Section 511) can be substituted by nish time minimization The processor selected in each step (see Eq (53)) is then Pmin P : tf (n, Pmin ) = min{max{tdr (n, P), tf (P)} + (n, P)}.



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Results from the previous section show that the MDR scheduler can achieve performance comparable to Optimal Smoothing, and yet can provide guaranteed video delivery in a mixed-traf c network. The trade-off, as Section 7.4.3 reveals, is the increased client buffer requirement, which in a few rare cases reaches close to 400 MB. While even this amount of client buffer can easily be accommodated in future home entertainment centers with built-in hard disk, there are still two inef ciencies. First, our results show that 78% of the video titles in our collection of 274 full-length video titles require no more than 20% of the worst-case buffer requirement. This suggests that the client buffer utilization will be low most of the time and most of the reserved buffer space will be unused. Second, although video titles with exceedingly large client buffer requirements are rare, the MDR scheduler cannot prevent such cases as the buffer requirement depends on the individual video s bit-rate pro le. In other words, the worst-case buffer requirement is, in theory, unbounded. To tackle these two de ciencies, we introduce in this section an Aggregated Monotonic Decreasing Rate (AMDR) scheduler that applies the MDR principle to aggregated network ows so that relaxed transmission schedules can be used to accommodate those rare video titles that otherwise require very large buffer requirements.





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Move the camera or the subject before taping When shooting people, place the subject s eyes one-third down from the

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The large buffer requirement in those rare videos is a result of the MDR scheduler s monotonicity property. However, as we proved in Section 7.3.4, the MDR scheduler already achieves minimum buffer requirement among all monotonic decreasing rate schedules, implying that the only way to reduce the buffer requirement is to relax the monotonicity requirement. Under the AMDR scheduler, the client buffer requirement, say B, is speci ed by the service provider as a design parameter. For videos with buffer requirements smaller than or equal to B, they are delivered using the original MDR schedules. By contrast, for videos that have buffer requirements larger than B, they are delivered using buffer-constrained schedules generated using temporal smoothing algorithms such as Optimal Smoothing. Now obviously temporal smoothing in general does not guarantee monotonicity and this implies that we can no longer guarantee video delivery for these videos in a mixed-traf c network. To tackle this problem, we can over-allocate bandwidth for these video streams such that the reserved bandwidth allocations are kept monotonic decreasing. For example, consider the transmission schedule {ri , Ti | i = 1, 2, . . . , n} generated by a temporal smoothing algorithm with buffer constraint B. For the rare videos, there exists i, j such that ri < r j for i < j. We can maintain the monotonicity property by applying the following procedure to the transmission schedule: si = if ri > ri+1 ri , , ri+1 , otherwise i = n 2, n 3, . . . , 1 (7.24)

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