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 da-ta from the storage system to the application. Our PPHSS strategy integrated with the predictive

Abstract—This paper presents a novel Predictive Energy-Aware Management (PEAM) system that is able to reduce the energy costs of storage systems by appropriately selecting data transmission methods. In particular, we evaluate the energy costs of three methods (1. transfer data without archiving and compression; 2. archive and transfer data; 3. compress and transfer data) in preliminary experiments. According to the results, we observe that the energy consumption of data transmission greatly varies case by case. We cannot simply apply one method in all cases. Therefore, we design an energy prediction model that can estimate the total energy cost of data transmission by using particular transmission methods. Based on the model, our predictive energy-aware management system can automatically select the most energy efficient method for data transmission. Our experimental results show that our system performs better than simply selecting any one among the three methods for data transmission in terms of energy efficiency. Keywords-predictive; energy-aware; storage system; I.