Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing.In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different “States”. We define the “State” of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of the real dataset show that the predictive ability of Prophet is improved amazingly, realizing a high recall and low false-positive rates while providing sufficient response time for the processing of failed SSDs.

High-densitysolid-state drives (SSDs), such as triple-level cell (TLC) or quad-level cell (QLC) flash, are adopted in parity-based RAID systems to achieve high reliability with low redundancy. However, the parity writes cause high write wear, which is unfriendly to such high-density SSDs with low write endurance. Conversely, high-performance SSDs, such as ZNAND, XL-Flash, have high write endurance but their high cost per bit hinders their deployment in RAID. This paper proposed a novel hybrid RAID structure, RAID45, to reduce parity writes for highdensity SSDs. Specifically, RAID45 uses high-performance SSD to store the parity of write-intensive stripes to absorb as much of the wear of parity writes on high-density SSDs as possible. Experimental results on real platform show that RAID45 achieves encouraging parity write reduction on the high-density SSDs.