Latent Sector Errors Research Articles

SWEP-RF: Accuracy sliding window-based ensemble pruning method for latent sector error prediction in cloud storage computing

Latent sector errors (LSEs) in disk drives cause significant outages, data loss, and unreliability in large-scale cloud storage systems, posing not only technical challenges but also environmental concerns in the context of carbon recycling. Predicting LSEs can help avoid these problems and improve cloud reliability, while also contributing to a more sustainable cloud infrastructure. Ensemble classifiers typically outperform individual classifiers for LSE prediction with high accuracy but can lead to underfitting and incurring additional computational cost, complexity, and time and memory consumption. This research addresses this challenge by proposing a twofold solution: optimizing the ensemble diversity of the resulting Random Forest (RF) classifier through accuracy sliding window-based ensemble pruning (SWEP-RF) and using this pruned ensemble to predict LSEs in cloud storage. By effectively predicting and mitigating LSEs, this approach reduces unnecessary energy consumption and carbon emissions associated with data recovery and reprocessing, aligning with carbon recycling goals. SWEP-RF maximizes its lower margin distribution to adapt the RF prediction performance and produce a strong-performing and effective subensemble, further enhancing the overall energy efficiency of cloud systems. Our approach also reduces ensemble size while maintaining high prediction accuracy, leading to more sustainable resource utilization. We evaluate our algorithm using datasets from Baidu Inc and Backblaze datacenters. Experimental results demonstrate that our approach achieves over 98.6% prediction accuracy, a low false alarm rate (FAR) of 0.003%, and extended meantime to data loss (MTTDL) with lead time in advance (LTA) of up to 383.4 Hrs. and 474.3 Hrs., respectively. SWEP-RF outperforms classical models and current state-of-the-art techniques in prediction accuracy, FAR, MTTDL, processing time, memory consumption, and cloud availability, highlighting its significance in not only enhancing cloud storage reliability but also reducing the carbon footprint of cloud services. Our method is a promising solution for enhancing cloud storage reliability through proactive LSE prediction, while addressing the urgent need for sustainable practices and carbon recycling in cloud computing.

Reliability Evaluation of Erasure-coded Storage Systems with Latent Errors

Large-scale storage systems employ erasure-coding redundancy schemes to protect against device failures. The adverse effect of latent sector errors on the Mean Time to Data Loss (MTTDL) and the Expected Annual Fraction of Data Loss (EAFDL) reliability metrics is evaluated. A theoretical model capturing the effect of latent errors and device failures is developed, and closed-form expressions for the metrics of interest are derived. The MTTDL and EAFDL of erasure-coded systems are obtained analytically for (i) the entire range of bit error rates; (ii) the symmetric, clustered, and declustered data placement schemes; and (iii) arbitrary device failure and rebuild time distributions under network rebuild bandwidth constraints. The range of error rates that deteriorate system reliability is derived analytically. For realistic values of sector error rates, the results obtained demonstrate that MTTDL degrades, whereas, for moderate erasure codes, EAFDL remains practically unaffected. It is demonstrated that, in the range of typical sector error rates and for very powerful erasure codes, EAFDL degrades as well. It is also shown that the declustered data placement scheme offers superior reliability.

Achieving high data reliability at low scrubbing cost via failure-aware scrubbing

Latent Sector Errors (LSEs) happen at a significant frequency in the field and can impose a huge risk to data reliability. Disk scrubbing is a background process that reads disks periodically to detect LSEs timely, thus shortening the window of vulnerability to data loss. Nowadays, proactive error prediction, using machine learning techniques, has been proposed to improve storage system reliability by increasing the scrubbing rate for disks with higher error rates. Unfortunately, the majority of works incur non-trivial scrubbing costs and overlook the relationship between complete disk failures and LSEs.In this paper, we attempt to maintain or improve data reliability at reduced scrubbing costs. In particular, we design a novel adaptive approach that enforces a lower scrubbing rate for healthy disks and a higher scrubbing rate for disks which are subject to LSEs. Besides LSEs that are specific to partial disk failures, we also adjust scrubbing rates according to complete disk failure rates, because disks typically develop LSEs before they finally fail. Moreover, a voting-based method that exploits the periodic characteristic of scrubbing is proposed to ensure prediction accuracy. Experimental results on a real-world field dataset have demonstrated the effectiveness of our proposed approach. Specifically, the results show that we can achieve the same level of reliability, in terms of Mean-Time-To-Detection (MTTD), as the traditional fixed-rate scrubbing scheme with almost 49% less scrubbing costs or we can improve the reliability by a factor of 2.4X without extra scrubbing costs. Compared with the state-of-the-art approaches, our method can achieve the same level of reliability with nearly 32% less scrubbing costs.

PFP: Improving the Reliability of Deduplication-based Storage Systems with Per-File Parity

Data deduplication weakens the reliability of storage systems since by design it removes duplicate data chunks common to different files and forces these files to share a single physical date chunk, or critical chunk, after deduplication. Thus, the loss of a single such critical data chunk can potentially render all referencing (sharing) files unavailable. However, the reliability issue in deduplication-based storage systems has not received adequate attention. Existing approaches introduce data redundancy after files have been deduplicated, either by replication on critical data chunks, i.e., chunks with high reference count, or RAID schemes on unique data chunks, which means that these schemes are based on individual unique data chunks rather than individual files. This can leave individual files vulnerable to losses, particularly in the presence of transient and unrecoverable data chunk errors such as latent sector errors. To address this file reliability issue, this paper proposes a Per-File Parity (short for PFP) scheme to improve the reliability of deduplication-based storage systems. PFP computes the XOR parity within parity groups of data chunks of each file after the chunking process but before the data chunks are deduplicated. Therefore, PFP can provide parity redundancy protection for all files by intra-file recovery and a higher-level protection for data chunks with high reference counts by inter-file recovery. Our reliability analysis and extensive data-driven, failure-injection based experiments conducted on a prototype implementation of PFP show that PFP significantly outperforms the existing redundancy solutions, DTR and RCR, in system reliability, tolerating multiple data chunk failures and guaranteeing file availability upon multiple data chunk failures. Moreover, a performance evaluation shows that PFP only incurs an average of 5.7 percent performance degradation to the deduplication-based storage system.

Rebuild processing in RAID5 with emphasis on the supplementary parity augmentation method[37

The rotated parity RAID5 disk array tolerates single disk failures by continuing operation by on-demand reconstruction of data blocks of the failed disk, until the systematic reconstruction of the contents of the failed disk is completed by the rebuild process on a spare disk. Supplementary Parity Augmentation (SPA), unlike the pyramid code, which has two parities covering half of the arrays disks each, extends RAID5's P parity with an additional S parity, which covers half of the disks. The extra load with respect to RAID5 of updating the S parity by one half of the disks is compensated by the more efficient on demand reconstructtion and rebuild processing when a disk fails. Although SPA has the same disk space redundancy level as RAID6, unlike RAID6 it can only deal with roughly half of all possible double disk failure cases for eight disks. For rebuild processing SPA reads half of the disks required by RAID5 and this leads to a higher Mean Time to Data Loss than RAID5, since fewer Latent Sector Errors are encountered. We review performance and reliability modeling of RAID5 arrays to provide insights into SPA's performance and reliability, which cannot be gained from numerical results alone. SPA is outperformed by the Intra-Disk Redundancy schemes combined with RAID5, which results in RAID6's reliability and RAID5 performance.

Online availability upgrades for parity-based RAIDs through supplementary parity augmentations

In this article, we propose a simple but powerful online availability upgrade mechanism, S upplementary P arity A ugmentations( SPA ), to address the availability issue in parity-based RAID systems. The basic idea of SPA is to store and update the supplementary parity units on one or a few newly augmented spare disks for online RAID systems in the operational mode, thus achieving the goals of improving the reconstruction performance while tolerating multiple disk failures and latent sector errors simultaneously. By applying the exclusive OR operations appropriately among supplementary parity, full parity, and data units, SPA can reconstruct the data on the failed disks with a fraction of the original overhead that is proportional to the supplementary parity coverage, thus significantly reducing the overhead of data regeneration and decreasing recovery time in parity-based RAID systems. Our extensive trace-driven simulation study shows that SPA can significantly improve the reconstruction performance of the RAID5 and RAID5+0 systems, at an acceptable performance overhead imposed in the operational mode. Moreover, our reliability analytical modeling and sequential Monte-Carlo simulation demonstrate that SPA is consistently more than double the MTTDL of the RAID5 system and improves the reliability of the RAID5+0 system noticeably.

Understanding latent sector errors and how to protect against them

Latent sector errors (LSEs) refer to the situation where particular sectors on a drive become inaccessible. LSEs are a critical factor in data reliability, since a single LSE can lead to data loss when encountered during RAID reconstruction after a disk failure or in systems without redundancy. LSEs happen at a significant rate in the field [Bairavasundaram et al. 2007], and are expected to grow more frequent with new drive technologies and increasing drive capacities. While two approaches, data scrubbing and intra-disk redundancy, have been proposed to reduce data loss due to LSEs, none of these approaches has been evaluated on real field data. This article makes two contributions. We provide an extended statistical analysis of latent sector errors in the field, specifically from the view point of how to protect against LSEs. In addition to providing interesting insights into LSEs, we hope the results (including parameters for models we fit to the data) will help researchers and practitioners without access to data in driving their simulations or analysis of LSEs. Our second contribution is an evaluation of five different scrubbing policies and five different intra-disk redundancy schemes and their potential in protecting against LSEs. Our study includes schemes and policies that have been suggested before, but have never been evaluated on field data, as well as new policies that we propose based on our analysis of LSEs in the field.

Effects of Data Scrubbing on Reliability in Storage Systems

Silent data corruptions, which are induced by latent sector errors, phantom writes, DMA parity errors and so on, can be detected by explicitly issuing a read command to a disk controller and comparing the corresponding data with their checksums. Because some of the data stored in a storage system may not be accessed for a long time, there is a high chance of silent data corruption occurring undetected, resulting in data loss. Therefore, periodic checking of the entire data in a storage system, known as data scrubbing, is essential to detect such silent data corruptions in time. The errors detected by data scrubbing will be recovered by the replica or the redundant information maintained to protect against permanent data loss. The longer the period between data scrubbings, the higher the probability of a permanent data loss. This paper proposes a Markov failure and repair model to conservatively analyze the effect of data scrubbing on the reliability of a storage system. We show the relationship between the period of a data scrubbing operation and the number of data replicas to manage the reliability of a storage system by using the proposed model.

An analysis of latent sector errors in disk drives

The reliability measures in today's disk drive-based storage systems focus predominantly on protecting against complete disk failures. Previous disk reliability studies have analyzed empirical data in an attempt to better understand and predict disk failure rates. Yet, very little is known about the incidence of latent sector errors i.e., errors that go undetected until the corresponding disk sectors are accessed. Our study analyzes data collected from production storage systems over 32 months across 1.53 million disks (both nearline and enterprise class). We analyze factors that impact latent sector errors, observe trends, and explore their implications on the design of reliability mechanisms in storage systems. To the best of our knowledge, this is the first study of such large scale our sample size is at least anorder of magnitude larger than previously published studies and the first one to focus specifically on latent sector errors and their implications on the design and reliability of storage systems.

IRON file systems

Commodity file systems trust disks to either work or fail completely, yet modern disks exhibit more complex failure modes. We suggest a new fail-partial failure model for disks, which incorporates realistic localized faults such as latent sector errors and block corruption. We then develop and apply a novel failure-policy fingerprinting framework, to investigate how commodity file systems react to a range of more realistic disk failures. We classify their failure policies in a new taxonomy that measures their Internal RObustNess (IRON) , which includes both failure detection and recovery techniques. We show that commodity file system failure policies are often inconsistent, sometimes buggy, and generally inadequate in their ability to recover from partial disk failures. Finally, we design, implement, and evaluate a prototype IRON file system, Linux ixt3, showing that techniques such as in-disk checksumming, replication, and parity greatly enhance file system robustness while incurring minimal time and space overheads.