Current disk arrays consist purely of hard disk drives, which normally provide huge storage capacities with low cost and high throughput for data-intensive applications. Nevertheless, they have some inherent disadvantages such as long access latencies and energy inefficiency due to their build-in mechanical mechanisms. Flash-memory-based solid state disks, on the other hand, although currently more expensive and inadequate in write cycles, offer much faster random read accesses and are much more robust and energy efficient. To combine the complementary merits of hard disks and flash disks, in this paper, we propose a hybrid disk array architecture named hybrid disk storage (HIT) for data-intensive applications. Next, a dynamic data redistribution strategy called performance, energy, and reliability balanced (PEARL), which can periodically redistribute data between flash disks and hard disks to adapt to the changing data access patterns, is developed on top of the HIT architecture. Comprehensive simulations using real-life traces demonstrate that compared with existing data placement techniques, PEARL exhibits its strength in both performance and energy consumption without impairing flash disk reliability.

The problem of statically assigning nonpartitioned files in a parallel I/O system has been extensively investigated. A basic workload characteristic assumption of most existing solutions to the problem is that there exists a strong inverse correlation between file access frequency and file size. In other words, the most popular files are typically small in size, while the large files are relatively unpopular. Recent studies on the characteristics of Web proxy traces suggested, however, the correlation, if any, is so weak that it can be ignored. Hence, the following two questions arise naturally. First, can existing algorithms still perform well when the workload assumption does not hold? Second, if not, can one develop a new file assignment strategy that is immune to the workload assumption? To answer these questions, we first evaluate the performance of three well-known file assignment algorithms with and without the workload assumption, respectively. Next, we develop a novel static nonpartitioned file assignment strategy for parallel I/O systems, called static round-robin (SOR), which is immune to the workload assumption. Comprehensive experimental results show that SOR consistently improves the performance in terms of mean response time over the existing schemes.

Mainstream energy conservation schemes for disk arrays inherently affect the reliability of disks. A thorough understanding of the relationship between energy saving techniques and disk reliability is still an open problem, which prevents effective design of new energy saving techniques and application of existing approaches in reliability-critical environments. As one step towards solving this problem, this paper presents an empirical reliability model, called Predictor of Reliability for Energy Saving Schemes (PRESS). Fed by three energy-saving-related reliability-affecting factors, operating temperature, utilization, and disk speed transition frequency, PRESS estimates the reliability of entire disk array. Further, a new energy saving strategy with reliability awareness called Reliability and Energy Aware Distribution (READ) is developed in the light of the insights provided by PRESS. Experimental results demonstrate that compared with existing energy saving schemes, MAID and PDC, READ consistently performs better in performance and reliability while achieving a comparable level of energy consumption.

To achieve energy-conservation and prompt responses simultaneously, in this paper we propose a novel energy-saving data placement strategy, called striping-based energy-aware (SEA), which can be applied to RAID-structured storage systems to noticeably save energy while providing quick responses. Further, to illustrate the effectiveness of SEA, we implement two SEA-powered RAID-based data placement algorithms, SEA0 and SEA5, by incorporating the SEA strategy into RAID-0 and RAID-5, respectively. Extensive experimental results demonstrate that compared with three well-known data placement algorithms greedy, SP, and HP, SEAO and SEA5 reduce mean response time on average at least 52.15% and 48.04% while saving energy on average no less than 10.12% and 9.35%, respectively.