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Existing caching strategies, in the storage domain, though well suited toexploit short range spatio-temporal patterns, are unable to leverage long-rangemotifs for improving hitrates. Motivated by this, we investigate novel Bayesiannon-parametric modeling(BNP) techniques for count vectors, to capture longrange correlations for cache preloading, by mining Block I/O traces. Suchtraces comprise of a sequence of memory accesses that can be aggregated intohigh-dimensional sparse correlated count vector sequences.  While there are several state of the art BNP algorithms for clustering andtheir temporal extensions for prediction, there has been no work on exploringthese for correlated count vectors. Our first contribution addresses this gapby proposing a DP based mixture model of Multivariate Poisson (DP-MMVP) and itstemporal extension(HMM-DP-MMVP) that captures the full covariance structure ofmultivariate count data. However, modeling full covariance structure for countvectors is computationally expensive, particularly for high dimensional data.Hence, we exploit sparsity in our count vectors, and as our main contribution,introduce the Sparse DP mixture of multivariate Poisson(Sparse-DP-MMVP),generalizing our DP-MMVP mixture model, also leading to more efficientinference. We then discuss a temporal extension to our model for cachepreloading.  We take the first step towards mining historical data, to capture long rangepatterns in storage traces for cache preloading. Experimentally, we show adramatic improvement in hitrates on benchmark traces and lay the groundwork forfurther research in storage domain to reduce latencies using data miningtechniques to capture long range motifs.

Mining Block I/O Traces for Cache Preloading with Sparse Temporal Non-parametric Mixture of Multivariate Poisson