A recent ultra-large SSD (e.g., a 32-TB SSD) provides many benefits in building cost-efficient enterprise storage systems. Owing to its large capacity, however, when such SSDs fail in a RAID storage system, a long rebuild overhead is inevitable for RAID reconstruction that requires a huge amount of data copies among SSDs. Motivated by modern SSD failure characteristics, we propose a new recovery scheme, called reparo , for a RAID storage system with ultra-large SSDs. Unlike existing RAID recovery schemes, reparo repairs a failed SSD at the NAND die granularity without replacing it with a new SSD, thus avoiding most of the inter-SSD data copies during a RAID recovery step. When a NAND die of an SSD fails, reparo exploits a multi-core processor of the SSD controller in identifying failed LBAs from the failed NAND die and recovering data from the failed LBAs. Furthermore, reparo ensures no negative post-recovery impact on the performance and lifetime of the repaired SSD. Experimental results using 32-TB enterprise SSDs show that reparo can recover from a NAND die failure about 57 times faster than the existing rebuild method while little degradation on the SSD performance and lifetime is observed after recovery.

The read-disturb problem is emerging as one of the main reliability issues in high-density NAND flash memory. A read-disturb error, which causes data loss, occurs to a page when a large number of reads are performed to its neighboring pages. In this paper, we propose a novel integrated approach for managing the read-disturb problem. Our approach is based on our key observations from the NAND physics that the read disturbance to neighboring pages is a function of the read voltage and the read time. Since the read disturbance has an exponential dependence on the read voltage, lowering the read voltage can improve the read-disturb resistance of a NAND block. By modifying NAND chips to support multiple read modes with different read voltages, our approach allows a flash translation layer module to exploit the tradeoff between the read disturbance and write speed. Since the read disturbance is also proportional to the read time, our approach exploits the difference in the read time among different NAND pages so that frequently read pages can be less intensively read-disturbed using fast page reads. By intelligently relocating read-intensive data to read-disturb resistant blocks and pages, our approach can reduce a large portion of the time overhead from managing read-disturb errors. We also propose a proactive data migration technique which is effective in reducing large variations in I/O response times of the existing on-demand read reclaim (RR) technique. Our experimental results show that our proposed techniques can reduce the execution time overhead by 73% over the existing read-disturb management technique while reducing I/O response time fluctuations during RR activations.