Predictive Prefetching for Parallel Hybrid Storage Systems

Abstract

In this paper, we present a predictive prefetching mechanism that is based on probability graph approach to perform prefetching between different levels in a parallel hybrid storage system. The fundamental concept of our approach is to invoke parallel hybrid storage system’s parallelism and prefetch data among multiple storage levels (e.g. solid state disks, and hard disk drives) in parallel with the application’s on-demand I/O reading requests. In this study, we show that a predictive prefetching across multiple storage levels is an efficient technique for placing near future needed data blocks in the uppermost levels near the application. Our PPHSS approach extends previous ideas of predictive prefetching in two ways: (1) our approach reduces applications’ execution elapsed time by keeping data blocks that are predicted to be accessed in the near future cached in the uppermost level; (2) we propose a parallel data fetching scheme in which multiple fetching mechanisms (i.e. predictive prefetching and application’s on-demand data requests) can work in parallel; where the first one fetches data blocks among the different levels of the hybrid storage systems (i.e. low-level (slow) to high-level (fast) storage devices) and the other one fetches the data from the storage system to the application. Our PPHSS strategy integrated with the predictive prefetching mechanism significantly reduces overall I/O access time in a hybrid storage system. Finally, we developed a simulator to evaluate the performance of the proposed predictive prefetching scheme in the context of hybrid storage systems. Our results show that our PPHSS can improve system performance by 4% across real-world I/O traces without the need of using large size caches.