Analytic modeling of clustered RAID with mapping based on nearly random permutation

A Redundant Array of Independent Disks (RAID) of G disks provides protection against single disk failures by adding one parity block for each G-1 data blocks. In a clustered RAID, the G data/parity blocks are distributed over a cluster of C disks (C>G), thus reducing the additional load on each disk due to a single disk failure. However, most methods proposed for implementing such a mapping do not work for general C and G values. In this paper, we describe a fast mapping algorithm based on almost-random permutations. An analytical model is constructed, based on the queue with a permanent customer, to predict recovery time and read/write performance. The accuracy of the results derived from this model is validated by comparing with simulations. Our analysis shows that clustered RAID is significantly more tolerant of disk failure than the basic RAID scheme. Both recovery time and performance degradation during recovery are substantially reduced in clustered RAID; moreover, these gains can be achieved using fairly small C/G ratios