Reliability Analysis of SSDs Under Power Fault

Zheng, Mai; Tucek, Joseph; Qin, Feng; Lillibridge, Mark; Zhao, Bill W.; Yang, Elizabeth S.

doi:10.1145/2992782

Cited by 16 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have built a preliminary prototype of X-Ray and applied it to diagnose 5 failure cases based on real bugs from the literature [101,70,76,63]. We discuss one case in details and summarize the results at the end.…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…Their initial experiments were performed on Linux kernel v2.6.32, and eight out of fifteen SSDs exhibited a symptom called "serialization errors" [100]. However, in their follow-up work where similar experiments were conducted on a newer kernel (v3.16.0) [101], the authors observed that the failure symptoms on some SSDs changed significantly (see Table 1, adapted from [101]). It was eventually confirmed that the different symptoms was caused by a syncrelated Linux kernel bug [101].…”

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

On Failure Diagnosis of the Storage Stack

Zhang,

Gatla,

Han

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Diagnosing storage system failures is challenging even for professionals. One example is the "When Solid State Drives Are Not That Solid" incident occurred at Algolia data center, where Samsung SSDs were mistakenly blamed for failures caused by a Linux kernel bug. With the system complexity keeps increasing, such obscure failures will likely occur more often.As one step to address the challenge, we present our on-going efforts called X-Ray. Different from traditional methods that focus on either the software or the hardware, X-Ray leverages virtualization to collects events across layers, and correlates them to generate a correlation tree. Moreover, by applying simple rules, X-Ray can highlight critical nodes automatically. Preliminary results based on 5 failure cases shows that X-Ray can effectively narrow down the search space for failures.

show abstract

Section: Preliminary Resultsmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

On Failure Diagnosis of the Storage Stack

Zhang,

Gatla,

Han

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…References [5,[8][9][10][11][12][13] studied the failure trend in flash chips. Other studies analyzed different factors in flash failures, such as read disturb [14][15][16][17], program disturb [9,18], data retention [10,19] and power faults [20,21]. Additionally, there are studies examining the influence of different factors on the error rate of flash chips [13].…”

Section: Related Workmentioning

confidence: 99%

Measurement and Analysis of SSD Reliability Data Based on Accelerated Endurance Test

et al. 2019

View full text Add to dashboard Cite

In recent years, NAND Flash-based solid-state drives (SSDs) have become more widely used in data centers and consumer markets. Data centers generally choose to provide high-quality storage services by deploying a large number of SSDs, but there are no effective preventive measures to reduce the impact of SSD failures currently. Some existing studies have analyzed the relevant factors related to SSD failures from different angles, but the characteristics of reliability changes exhibited by SSD throughout the life cycle have not been explored in depth. On the other hand, although the 3D manufacturing process has increased the storage density of the SSD, the mutual influence between the flash units has also increased, resulting in severe degradation of the performance and lifetime of the SSD. Therefore, in order to fully understand the reliability varying process of SSD throughout the life cycle, we first designed an SSD lifetime endurance test method, then conducted the endurance test and collected the reliability data for the entire life cycle of the 3D TLC SSD in the laboratory environment with reference to the JEDEC standard. Through the analysis of experimental data and its statistical correlation, it is found that SSD will produce a large number of uncorrectable errors before reaching the endurance limit, and there will be a phenomenon of continuous high operating temperature, as well as showing some intrinsic relationships about SSD reliability data. The findings in this paper are valuable for identifying whether an SSD is going to fail.

show abstract

“…As shown in Table 4, each record contained a checksum to detect corruption within the record. Furthermore, the record included self-identification information (e.g., the expected range and LBA) to allow detection of misplacement of the record (e.g., flying writes [21,22]), which may pass the examination of the checksum. The other fields (e.g., size and rand) allowed us to regenerate the exact record for verification uniquely.…”

Section: Location Of Writementioning

confidence: 99%

iLife: Safely Extending Lifetime for Memory-Oriented SSD

2019

Electronics

View full text Add to dashboard Cite

Currently, the roles of SSDs have been diversified significantly. Apart for storage purposes, users can also utilize flash drives to cache hot data or buffering incoming writes to achieve high throughput. In this case, the endurance study of Solid State Drives (SSDs) has become an increasingly important topic as users rely on such research to reliably avoid early retirement of their old drives in laptops or machines in data centers. However, current manufacturers adopt Program/Erase (P/E) cycle-based life indicators to estimate the remaining lifetime of an SSD, which is often too conservative due to longtime data retention concerns. In this paper, we begin by analyzing the inaccuracy and other potential issues of existing P/E cycle-based life indicators for SSDs under memory-oriented workloads. To construct an accurate life indicator, we conduct a more extensive and sophisticated experiment to wear out eight SSDs from four different vendors. By monitoring the device status extracted from the standard S.M.A.R.T.attributes in the experiment, we make eleven interesting findings on the relationship between the device lifetime and different types of errors observed. Based on our unique findings, we propose iLife, a pattern-based life indicator for SSDs under memory-oriented workloads. Our evaluation of iLife on a different set of six SSDs shows that iLife is a more accurate life indicator, with an average of 14.2% higher accuracy on lifetime estimation and up to 21.2% safe lifetime extension compared to existing P/E cycle-based indicators.

show abstract

Reliability Analysis of SSDs Under Power Fault

Cited by 16 publications

References 32 publications

On Failure Diagnosis of the Storage Stack

On Failure Diagnosis of the Storage Stack

Measurement and Analysis of SSD Reliability Data Based on Accelerated Endurance Test

iLife: Safely Extending Lifetime for Memory-Oriented SSD

Contact Info

Product

Resources

About