NAND based solid state drives (SSDs) are almost ubiquitously used in safety-critical systems, and recent advances have demonstrated RAID implementations that built on the top of SSDs can effectively enhance the data integrity and reliability. RAID can restore the lost data chunks in case of failures of RAID components (i.e., SSDs in the context), through a process of RAID reconstruction. Specially, online RAID reconstruction allows the RAID system to continue fulfilling user I/O requests during reconstruction. Servicing user I/O requests, however, significantly affects the performance of reconstruction due to contention for the shared SSD bandwidth. This paper proposes a fast online reconstruction method for SSD-based RAID systems, that preferably restores the lost chunks if the replaced SSD device is idle to reduce the reconstruction time, thus minimizing the probability of a second disk failure in the RAID system during reconstruction. Furthermore, it schedules the tasks of restoring data/parity chunks according to the their impacts on other working SSDs in the RAID system, for the purpose of reducing the overall I/O latency. Through a series of experiments based on the selected disk traces of real-world applications, we show that the proposed reconstruction scheme can reduce the reconstruction time by up to 45.6%, and meanwhile cut down the I/O latency by 9.8% on average compared to state-of-the-art methods.

The security of cloud-based databases is a crucial topic in current research. The primary solutions are focused on access control and data encryption. A central challenge these solutions face is navigating the balance between system security and operational efficiency. However, individualized data privacy requires fine-grained data protection at columns/rows or individual elements. In existing fine-grained resource or policy protection methods, resource access predominantly uses a traversal method, leading to a linear growth in time overhead. In response to these issues, this paper presents a fine-grained access control mechanism based on search trees. This mechanism provides element-level resource protection and accelerates policy search using an index tree, thereby reducing the time overhead of the fine-grained access control system. Experimental results demonstrate that this mechanism achieves element-level resource protection while maintaining low-performance overhead.

Most flash-based solid-state drives (SSDs) adopt an onboard dynamic random access memory (DRAM) to buffer hot write data. Then, the write or overwrite operations can be absorbed by the DRAM cache, given that there is sufficient locality in the applications’ I/O access pattern, to consequently avoid flushing the write data onto underlying SSD cells. After analyzing typical real-world workloads over SSDs, we observed that the buffered data of small-size requests are more likely to be reaccessed than those of large write requests. To efficiently utilize the limited space of DRAM cache, this article proposes an adaptive request granularity-based cache management scheme for SSDs. First, we introduce a request block corresponding to a write request, as the cache management granularity, and propose a dynamic manner for classifying small and large request blocks. Next, we design three-level linked lists for supporting different routines of upgradation for small and large request blocks, once their data have been hit in the cache. Finally, we present a scheme of evicting the request blocks having the minimum cost in cache replacement, by taking both factors of access hotness and time discounting into account. Experimental results show that our proposal can yield improvements on cache hits and the overall I/O latency by21.8% and14.7% on average, compared to state-of-the-art cache management schemes inside SSDs.

Most flash-based solid-state drives (SSDs) employ an on-board Dynamic Random Access Memory (DRAM) to cache hot data at the SSD page granularity. This can significantly reduce the number of flush operations to the underlying arrays of SSDs given that there is sufficient locality in the applications’ I/O access pattern. We observe, however, that in most I/O workloads over SSDs the buffered data of small sized requests are more likely to be re-accessed than those of larger requests, which also require more DRAM space for caching their data.