Performability Modeling for RAID Storage Systems by Markov Regenerative Process

This paper presents a performability model for RAID storage systems using Markov regenerative process to compare different RAID architectures. While homogeneous Markov models are extensively used for reliability analysis of RAID storage systems, the memory-less property of the sojourn time assumed in such models is not satisfied in reality, especially in disk rebuild process whose progress is not interrupted even at an event of another disk failure. In this paper, we use Markov regenerative process which allows us to model the generally distributed rebuild times providing a needed extension of the traditional Markov models. The Markov regenerative process is then used to assess the performability of the storage system by assigning reward rates to each state based on the real storage benchmark results. Our numerical study characterizes the performability advantage of RAID6 architecture over RAID10 architecture in terms of sequential read access. Our findings include that the effect of exponential assumption for the rebuild times has practically negligible effect when we focus on data availability. However, the effect this approximation on performability prediction may not be negligible especially when the performance level drastically changes in degraded states. Our MRGP model provides more accurate prediction of performability in such cases.