초고용량 솔리드 스테이드 드라이브를 위한 신뢰성 향상 및 성능 최적화 기술

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. 김지홍.The development of ultra-large NAND flash storage devices (SSDs) is recently made possible by NAND flash memory semiconductor process scaling and multi-leveling techniques, and NAND package technology, which enables continuous increasing of storage capacity by mounting many NAND flash memory dies in an SSD. As the capacity of an SSD increases, the total cost of ownership of the storage system can be reduced very effectively, however due to limitations of ultra-large SSDs in reliability and performance, there exists some obstacles for ultra-large SSDs to be widely adopted. In order to take advantage of an ultra-large SSD, it is necessary to develop new techniques to improve these reliability and performance issues. In this dissertation, we propose various optimization techniques to solve the reliability and performance issues of ultra-large SSDs. In order to overcome the optimization limitations of the existing approaches, our techniques were designed based on various characteristic evaluation results of NAND flash devices and field failure characteristics analysis results of real SSDs. We first propose a low-stress erase technique for the purpose of reducing the characteristic deviation between wordlines (WLs) in a NAND flash block. By reducing the erase stress on weak WLs, it effectively slows down NAND degradation and improves NAND endurance. From the NAND evaluation results, the conditions that can most effectively guard the weak WLs are defined as the gerase mode. In addition, considering the user workload characteristics, we propose a technique to dynamically select the optimal gerase mode that can maximize the lifetime of the SSD. Secondly, we propose an integrated approach that maximizes the efficiency of copyback operations to improve performance while not compromising data reliability. Based on characterization using real 3D TLC flash chips, we propose a novel per-block error propagation model under consecutive copyback operations. Our model significantly increases the number of successive copybacks by exploiting the aging characteristics of NAND blocks. Furthermore, we devise a resource-efficient error management scheme that can handle successive copybacks where pages move around multiple blocks with different reliability. By utilizing proposed copyback operation for internal data movement, SSD performance can be effectively improved without any reliability issues. Finally, we propose a new recovery scheme, called reparo, for a RAID storage system with ultra-large SSDs. Unlike the 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 to identify failed LBAs from the failed NAND die and to recover data from the failed LBAs. Furthermore, reparo ensures no negative post-recovery impact on the performance and lifetime of the repaired SSD. In order to evaluate the effectiveness of the proposed techniques, we implemented them in a storage device prototype, an open NAND flash storage device development environment, and a real SSD environment. And their usefulness was verified using various benchmarks and I/O traces collected the from real-world applications. The experiment results show that the reliability and performance of the ultra-large SSD can be effectively improved through the proposed techniques.반도체 공정의 미세화, 다치화 기술에 의해서 지속적으로 용량이 증가하고 있는 단위 낸드 플래쉬 메모리와 하나의 낸드 플래쉬 기반 스토리지 시스템 내에 수 많은 낸드 플래쉬 메모리 다이를 실장할 수 있게하는 낸드 패키지 기술로 인해 하드디스크보다 훨씬 더 큰 초고용량의 낸드 플래쉬 저장장치의 개발을 가능하게 했다. 플래쉬 저장장치의 용량이 증가할 수록 스토리지 시스템의 총 소유비용을 줄이는데 매우 효과적인 장점을 가지고 있으나, 신뢰성 및 성능의 측면에서의 한계로 인해서 초고용량 낸드 플래쉬 저장장치가 널리 사용되는데 있어서 장애물로 작용하고 있다. 초고용량 저장장치의 장점을 활용하기 위해서는 이러한 신뢰성 및 성능을 개선하기 위한 새로운 기법의 개발이 필요하다. 본 논문에서는 초고용량 낸드기반 저장장치(SSD)의 문제점인 성능 및 신뢰성을 개선하기 위한 다양한 최적화 기술을 제안한다. 기존 기법들의 최적화 한계를 극복하기 위해서, 우리의 기술은 실제 낸드 플래쉬 소자에 대한 다양한 특성 평가 결과와 SSD의 현장 불량 특성 분석결과를 기반으로 고안되었다. 이를 통해서 낸드의 플래쉬 특성과 SSD, 그리고 호스트 시스템의 동작 특성을 고려한 성능 및 신뢰성을 향상시키는 최적화 방법론을 제시한다. 첫째로, 본 논문에서는 낸드 플래쉬 불록내의 페이지들간의 특성편차를 줄이기 위해서 동적인 소거 스트레스 경감 기법을 제안한다. 제안된 기법은 낸드 블록의 내구성을 늘리기 위해서 특성이 약한 페이지들에 대해서 더 적은 소거 스트레스가 인가할 수 있도록 낸드 평가 결과로 부터 소거 스트레스 경감 모델을 구축한다. 또한 사용자 워크로드 특성을 고려하여, 소거 스트레스 경감 기법의 효과가 최대화 될 수 있는 최적의 경감 수준을 동적으로 판단할 수 있도록 한다. 이를 통해서 낸드 블록을 열화시키는 주요 원인인 소거 동작을 효율적으로 제어함으로써 저장장치의 수명을 효과적으로 향상시킨다. 둘째로, 본 논문에서는 고용량 SSD에서의 내부 데이터 이동으로 인한 성능 저하문제를 개선하기 위해서 낸드 플래쉬의 제한된 카피백(copyback) 명령을 활용하는 적응형 기법인 rCPB을 제안한다. rCPB은 Copyback 명령의 효율성을 극대화 하면서도 데이터 신뢰성에 문제가 없도록 낸드의 블럭의 노화특성을 반영한 새로운 copyback 오류 전파 모델을 기반으로한다. 이에더해, 신뢰성이 다른 블럭간의 copyback 명령을 활용한 데이터 이동을 문제없이 관리하기 위해서 자원 효율적인 오류 관리 체계를 제안한다. 이를 통해서 신뢰성에 문제를 주지 않는 수준에서 copyback을 최대한 활용하여 내부 데이터 이동을 최적화 함으로써 SSD의 성능향상을 달성할 수 있다. 마지막으로, 본 논문에서는 초고용량 SSD에서 낸드 플래쉬의 다이(die) 불량으로 인한 레이드(redundant array of independent disks, RAID) 리빌드 오버헤드를 최소화 하기위한 새로운 RAID 복구 기법인 reparo를 제안한다. Reparo는 SSD에 대한 교체없이 SSD의 불량 die에 대해서만 복구를 수행함으로써 복구 오버헤드를 최소화한다. 불량이 발생한 die의 데이터만 선별적으로 복구함으로써 복구 과정의 리빌드 트래픽을 최소화하며, SSD 내부의 병렬구조를 활용하여 불량 die 복구 시간을 효과적으로 단축한다. 또한 die 불량으로 인한 물리적 공간감소의 부작용을 최소화 함으로써 복구 이후의 성능 저하 및 수명의 감소 문제가 없도록 한다. 본 논문에서 제안한 기법들은 저장장치 프로토타입 및 공개 낸드 플래쉬 저장장치 개발환경, 그리고 실장 SSD환경에 구현되었으며, 실제 응용 프로그램을 모사한 다양한 벤트마크 및 실제 I/O 트레이스들을 이용하여 그 유용성을 검증하였다. 실험 결과, 제안된 기법들을 통해서 초고용량 SSD의 신뢰성 및 성능을 효과적으로 개선할 수 있음을 확인하였다.I Introduction 1 1.1 Motivation 1 1.2 Dissertation Goals 3 1.3 Contributions 5 1.4 Dissertation Structure 8 II Background 11 2.1 Overview of 3D NAND Flash Memory 11 2.2 Reliability Management in NAND Flash Memory 14 2.3 UL SSD architecture 15 2.4 Related Work 17 2.4.1 NAND endurance optimization by utilizing page characteristics difference 17 2.4.2 Performance optimizations using copyback operation 18 2.4.3 Optimizations for RAID Rebuild 19 2.4.4 Reliability improvement using internal RAID 20 III GuardedErase: Extending SSD Lifetimes by Protecting Weak Wordlines 22 3.1 Reliability Characterization of a 3D NAND Flash Block 22 3.1.1 Large Reliability Variations Among WLs 22 3.1.2 Erase Stress on Flash Reliability 26 3.2 GuardedErase: Design Overview and its Endurance Model 28 3.2.1 Basic Idea 28 3.2.2 Per-WL Low-Stress Erase Mode 31 3.2.3 Per-Block Erase Modes 35 3.3 Design and Implementation of LongFTL 39 3.3.1 Overview 39 3.3.2 Weak WL Detector 40 3.3.3 WAF Monitor 42 3.3.4 GErase Mode Selector 43 3.4 Experimental Results 46 3.4.1 Experimental Settings 46 3.4.2 Lifetime Improvement 47 3.4.3 Performance Overhead 49 3.4.4 Effectiveness of Lowest Erase Relief Ratio 50 IV Improving SSD Performance Using Adaptive Restricted- Copyback Operations 52 4.1 Motivations 52 4.1.1 Data Migration in Modern SSD 52 4.1.2 Need for Block Aging-Aware Copyback 53 4.2 RCPB: Copyback with a Limit 55 4.2.1 Error-Propagation Characteristics 55 4.2.2 RCPB Operation Model 58 4.3 Design and Implementation of rcFTL 59 4.3.1 EPM module 60 4.3.2 Data Migration Mode Selection 64 4.4 Experimental Results 65 4.4.1 Experimental Setup 65 4.4.2 Evaluation Results 66 V Reparo: A Fast RAID Recovery Scheme for Ultra- Large SSDs 70 5.1 SSD Failures: Causes and Characteristics 70 5.1.1 SSD Failure Types 70 5.1.2 SSD Failure Characteristics 72 5.2 Impact of UL SSDs on RAID Reliability 74 5.3 RAID Recovery using Reparo 77 5.3.1 Overview of Reparo 77 5.4 Cooperative Die Recovery 82 5.4.1 Identifier: Parallel Search of Failed LBAs 82 5.4.2 Handler: Per-Core Space Utilization Adjustment 83 5.5 Identifier Acceleration Using P2L Mapping Information 89 5.5.1 Page-level P2L Entrustment to Neighboring Die 90 5.5.2 Block-level P2L Entrustment to Neighboring Die 92 5.5.3 Additional Considerations for P2L Entrustment 94 5.6 Experimental Results 95 5.6.1 Experimental Settings 95 5.6.2 Experimental Results 97 VI Conclusions 109 6.1 Summary 109 6.2 Future Work 111 6.2.1 Optimization with Accurate WAF Prediction 111 6.2.2 Maximizing Copyback Threshold 111 6.2.3 Pre-failure Detection 112박

RAID Organizations for Improved Reliability and Performance: A Not Entirely Unbiased Tutorial (1st revision)

RAID proposal advocated replacing large disks with arrays of PC disks, but as the capacity of small disks increased 100-fold in 1990s the production of large disks was discontinued. Storage dependability is increased via replication or erasure coding. Cloud storage providers store multiple copies of data obviating for need for further redundancy. Varitaions of RAID based on local recovery codes, partial MDS reduce recovery cost. NAND flash Solid State Disks - SSDs have low latency and high bandwidth, are more reliable, consume less power and have a lower TCO than Hard Disk Drives, which are more viable for hyperscalers.Comment: Submitted to ACM Computing Surveys. arXiv admin note: substantial text overlap with arXiv:2306.0876

Studies of disk arrays tolerating two disk failures and a proposal for a heterogeneous disk array

There has been an explosion in the amount of generated data in the past decade. Online access to these data is made possible by large disk arrays, especially in the RAID (Redundant Array of Independent Disks) paradigm. According to the RAID level a disk array can tolerate one or more disk failures, so that the storage subsystem can continue operating with disk failure(s). RAID 5 is a single disk failure tolerant array which dedicates the capacity of one disk to parity information. The content on the failed disk can be reconstructed on demand and written onto a spare disk. However, RAID5 does not provide enough protection for data since the data loss may occur when there is a media failure (unreadable sectors) or a second disk failure during the rebuild process. Due to the high cost of downtime in many applications, two disk failure tolerant arrays, such as RAID6 and EVENODD, have become popular. These schemes use 2/N of the capacity of the array for redundant information in order to tolerate two disk failures. RM2 is another scheme that can tolerate two disk failures, with slightly higher redundancy ratio. However, the performance of these two disk failure tolerant RAID schemes is impaired, since there are two check disks to be updated for each write request. Therefore, their performance, especially when there are disk failure(s), is of interest. In the first part of the dissertation, the operations for the RAID5, RAID6, EVENODD and RM2 schemes are described. A cost model is developed for these RAID schemes by analyzing the operations in various operating modes. This cost model offers a measure of the volume of data being transmitted, and provides adevice-independent comparison of the efficiency of these RAID schemes. Based on this cost model, the maximum throughput of a RAID scheme can be obtained given detailed disk characteristic and RAID configuration. Utilizing M/G/1 queuing model and other favorable modeling assumptions, a queuing analysis to obtain the mean read response time is described. Simulation is used to validate analytic results, as well as to evaluate the RAID systems in analytically intractable cases. The second part of this dissertation describes a new disk array architecture, namely Heterogeneous Disk Array (HDA). The HDA is motivated by a few observations of the trends in storage technology. The HDA architecture allows a disk array to have two forms of heterogeneity: (1) device heterogeneity, i.e., disks of different types can be incorporated in a single HDA; and (2) RAID level heterogeneity, i.e., various RAID schemes can coexist in the same array. The goal of this architecture is (1) utilizing the extra resource (i.e. bandwidth and capacity) introduced by new disk drives in an automated and efficient way; and (2) using appropriate RAID levels to meet the varying availability requirements for different applications. In HDA, each new object is associated with an appropriate RAID level and the allocation is carried out in a way to keep disk bandwidth and capacity utilizations balanced. Design considerations for the data structures of HDA metadata are described, followed by the actual design of the data structures and flowcharts for the most frequent operations. Then a data allocation algorithm is described in detail. Finally, the HDA architecture is prototyped based on the DASim simulation toolkit developed at NJIT and simulation results of an HDA with two RAID levels (RAID 1 and RAIDS) are presented

Efficient data reliability management of cloud storage systems for big data applications

Cloud service providers are consistently striving to provide efficient and reliable service, to their client's Big Data storage need. Replication is a simple and flexible method to ensure reliability and availability of data. However, it is not an efficient solution for Big Data since it always scales in terabytes and petabytes. Hence erasure coding is gaining traction despite its shortcomings. Deploying erasure coding in cloud storage confronts several challenges like encoding/decoding complexity, load balancing, exponential resource consumption due to data repair and read latency. This thesis has addressed many challenges among them. Even though data durability and availability should not be compromised for any reason, client's requirements on read performance (access latency) may vary with the nature of data and its access pattern behaviour. Access latency is one of the important metrics and latency acceptance range can be recorded in the client's SLA. Several proactive recovery methods, for erasure codes are proposed in this research, to reduce resource consumption due to recovery. Also, a novel cache based solution is proposed to mitigate the access latency issue of erasure coding

Optimizations for Energy-Aware, High-Performance and Reliable Distributed Storage Systems

With the decreasing cost and wide-spread use of commodity hard drives, it has become possible to create very large-scale storage systems with less expense. However, as we approach exabyte-scale storage systems, maintaining important features such as energy-efficiency, performance, reliability and usability became increasingly difficult. Despite the decreasing cost of storage systems, the energy consumption of these systems still needs to be addressed in order to retain cost-effectiveness. Any improvements in a storage system can be outweighed by high energy costs. On the other hand, large-scale storage systems can benefit more from the object storage features for improved performance and usability. One area of concern is metadata performance bottleneck of applications reading large directories or creating a large number of files. Similarly, computation on big data where data needs to be transferred between compute and storage clusters adversely affects I/O performance. As the storage systems become more complex and larger, transferring data between remote compute and storage tiers becomes impractical. Furthermore, storage systems implement reliability typically at the file system or client level. This approach might not always be practical in terms of performance. Lastly, object storage features are usually tailored to specific use cases that makes it harder to use them in various contexts. In this thesis, we are presenting several approaches to enhance energy-efficiency, performance, reliability and usability of large-scale storage systems. To begin with, we improve the energy-efficiency of storage systems by moving I/O load to a subset of the storage nodes with energy-aware node allocation methods and turn off the unused nodes, while preserving load balance on demand. To address the metadata performance issue associated with large creates and directory reads, we represent directories with object storage collections and implement lazy creation of objects. Similarly, in-situ computation on large-scale data is enabled by using object storage features to integrate a computational framework with the existing object storage layer to eliminate the need to transfer data between compute and storage silos for better performance. We then present parity-based redundancy using object storage features to achieve reliability with less performance impact. Finally, unified storage brings together the object storage features to meet the needs of distinct use cases; such as cloud storage, big data or high-performance computing to alleviate the unnecessary fragmentation of storage resources. We evaluate each proposed approach thoroughly and validate their effectiveness in terms of improving energy-efficiency, performance, reliability and usability of a large-scale storage system

Data partitioning and load balancing in parallel disk systems

Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent file system that optimizes striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces

Sixth Goddard Conference on Mass Storage Systems and Technologies Held in Cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems

This document contains copies of those technical papers received in time for publication prior to the Sixth Goddard Conference on Mass Storage Systems and Technologies which is being held in cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems at the University of Maryland-University College Inn and Conference Center March 23-26, 1998. As one of an ongoing series, this Conference continues to provide a forum for discussion of issues relevant to the management of large volumes of data. The Conference encourages all interested organizations to discuss long term mass storage requirements and experiences in fielding solutions. Emphasis is on current and future practical solutions addressing issues in data management, storage systems and media, data acquisition, long term retention of data, and data distribution. This year's discussion topics include architecture, tape optimization, new technology, performance, standards, site reports, vendor solutions. Tutorials will be available on shared file systems, file system backups, data mining, and the dynamics of obsolescence