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Performance is measured by the number of data blocks that need to be ent to the redundant nodes for redundant data update or simply called redundant data update overhead The total number of data blocks is 40,000 and is xed throughout the simulation For simplicity the redundant data update overhead for updating one redundant node is presented We will return to the issue of updating multiple redundant data nodes in Section 155 In the rst experiment, we investigate the redundant data update overhead in continuous system growth We begin with a system of ve data nodes and one redundant node Then we add a new node to the system one by one, each time the redundant data blocks are completely updated using the SRDU algorithm This continues until the system grows to 400 data nodes Figure 15.

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13 plots the redundant data update overhead versus data node size from 6 to 400 As expected, the simple algorithm (sending all data blocks to the redundant node) performs the worst On the other hand, regenerating redundant data using a centralized archive server incurs the least overhead These two curves serve respectively as the upper bound and lower bound for redundant data update overhead Surprisingly, direct reuse of the original redundant data does not result in signi cant savings This is because the algorithm maintains the same data order within the parity group in computing the redundant data, and thus signi cantly reduces the opportunity for redundant data reuse Once this restriction is relaxed by reshuf ing the parity group, the redundant data update overhead is reduced by half to around 20,000 blocks Reuse of cached data blocks further reduces the overhead by half to around 10,000 blocks.

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With all three techniques applied, the SRDU algorithm can reduce the redundant data update overhead by as much as 70% In the previous experiment, we completely update all redundant data blocks before adding another new node Clearly this is inef cient if new nodes are added frequently or added to the system in a batch To address this issue, we conduct a second experiment where multiple nodes, say W , are added to the system before the redundant data are updated Figure 1514 plots the redundant data update overhead versus the batch size W for initial system size of 80 nodes The results show that we can reduce the per-node redundant data update overhead signi cantly by batching redundant data update for multiple new nodes h-1 Paint Data Matrix 2d Barcode In Visual Basic NET Using Barcode generator for NET Related: .

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The models are vetted by comparing the performance measures they induce in a simulation to the performance measures induced y data traces. All of these models are Markov chains, so they are short-range dependent (SRD). Hurst parameter estimates for the time series these models describe indicate the presence of long-range dependence. The reasons that short-range dependent models can provide good models for time series that exhibit long-range dependence are given in detail in Section 1.4. Our results are summarized in the last section. 12.2 VIDEO CONFERENCES. Lag (frames). Paint Code-128 In Visual Basic .NET Using Barcode generator for .NET .Related: 

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