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Abstract : |
{mzwang, natassa, christos}cs. cmu. edu Traffic, like disk and memory accesses, typically exhibits burstiness, temporal locality and spatial locality. There is much recent ground-breaking work on temporal modeling (self-similarity etc), on disk and web traffic, with several statistical models that generate realistic series of time-stamps. However, no work generates realistic traces for both time and location (eg., block-id). In fact, except for qualitative speculations, it is not even known whether/how the time-stamps are correlated with the locations, nor how to measure this correlation, let alone how to re-produce it realistically. These are exactly the problems we solve here: (a) We propose the 'entropy plots ' to quantify the spatial/temporal correlation (or lack of it), and (b) we propose a new model, the 'PQR[%bookAbstract%]#039; model, that captures all the characteristics of real spario-temporal traffic. Our model can generate traffic that is bursty (or uniform) on time; bursty or uniform on space; and it can mimic the correlation between space and time, whenever such correlation exists. Moreover, it requires very few parameters (p, q, r, and the grand total of disk/memory accesses); and it has linear scalability in computing these parameters. Experiments with multiple real data sets (disk traces from HP Labs, TPC-C memory traces), show that our model can mimic real traces very well, while the only obvious alternative, the independence assumption, leads to more than 60x worse, |
