Existing caching strategies, in the storage domain, though well suited to exploit short range spatio-temporal patterns, are unable to leverage longrange motifs for improving hitrates. Motivated by this, we investigate novel Bayesian nonparametric modeling(BNP) techniques for count vectors, to capture long range correlations for cache preloading, by mining Block I/O traces.
Traces comprise of a sequence of memory accesses that can be aggregated into high dimensional sparse correlated count vector sequences. While there are several state of the art BNP algorithms for clustering and their temporal extensions for prediction, there has been no work on exploring these for correlated count vectors. We introduce the Sparse-Multivariate-Poisson and address this gap by proposing novel DP based mixture models of Multivariate Poisson and their temporal extensions exploiting the sparsity of data to design efficient inference algorithms.
We take the first step towards mining historical data, to capture long range patterns in storage traces for cache preloading. Experimentally, we show a dramatic improvement in hitrates on benchmark traces and lay the groundwork for further research in storage domain to reduce latencies using data mining techniques to capture long range motifs.