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If we divide the media blocks into groups of LCM(n 1, n) blocks, where the function LCM(n 1, n) computes the least common multiple of n 1 and n, then the rst (n 1) blocks in each group will not need to be moved (see Figure 151 for an example) All other blocks in the group will need to be relocated Therefore, the reorganization overhead in round-robin placement is given by B LCM(n 1, n) (n 1) LCM(n 1, n) =B (n 1)n (n 1) (n 1)n 1 n (151).



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solution accuracy Specifically, neglecting all but the linear terms in a Taylorseries expansion of Eq (5-13) produces the linearized perturbation equations

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= B 1

(5-14)





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For the SCADDAR algorithm the reorganization overhead has been shown to approach the theoretical lower bound of B/n and the exact values can be obtained from simulated calculations. Similarly, we also obtain the reorganization overhead of m-RPDR using simulated calculations. Figure 15.8 compares the reorganization overhead of round-robin, SCADDAR, and m-RPDR for m = 1, 2, 5, and 10. There are three observations. First, the round-robin algorithm and the SCADDAR algorithm have the highest and lowest reorganization overhead respectively. Moreover, the differences

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Reorganization Overhead (blocks)

Control-system designers did not actually need the noisy solutions x = x(t) of the perturbation equations (5-14) What they really wanted was a small number of mean-square perturbations

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10 0 50 100 System Size (nodes) 150 200

XX E{ x2(t1)}

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increase when the system grows larger. This is because for the SCADDAR algorithm the overheads approximately equal to B/n and so decreases as the system grows. By contrast, overheads of the round-robin algorithm approach B as n increases (cf. equation (15.1)). Second, the reorganization overheads of the m-RPDR algorithms are lower than the roundrobin algorithm but higher than the SCADDAR algorithm. In particular, a larger value of m will result in lower reorganization overhead, thus providing a tool to trade off streaming load balance for lower reorganization overhead. Third, the 1-RPDR algorithm can achieve lower overhead than the round-robin algorithm and yet can still achieve perfect streaming load balance. Thus, the 1-RPDR algorithm can be used in place of the round-robin algorithm whenever perfect streaming load balance is required. So far we have assumed that the system is reorganized whenever a new node is added to the system. In practice, it may be desirable to add multiple nodes to the system at the same time to amortize the administrative overheads. Interestingly, performing data reorganization simultaneously for multiple nodes can also result in lower per-node reorganization overhead compared to adding them one by one. Figure 15.9 illustrates the potential savings from performing data reorganization for k = 1, 2, 3, and 5 new nodes. The results clearly show that the per-node reorganization overhead decreases signi cantly (note that the vertical axis is in logarithm scale) for larger values of k. Moreover, the savings are most signi cant when switching from k = 1 to k = 2, suggesting that adding nodes to the system two-at-a-time can be an ef cient strategy to expand the system incrementally.

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