Dynamic Virtual Page-based Flash Translation Layer with Novel Hot Data Identification and Adaptive Parallelism Management

Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of NAND flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data pre-classifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of NAND flash devices with improved GC-costs, compared with related works.Peer reviewe

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe

On the use of NAND flash memory in high-performance relational databases

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 47-49).High-density NAND flash storage has become relatively inexpensive due to the popularity of various consumer electronics. Recently, several manufacturers have released IDE-compatible NAND flash-based drives in sizes up to 64 GB at reasonable (sub-$1000) prices. Because flash is significantly more durable than mechanical hard drives and requires considerably less energy, there is some speculation that large data centers will adopt these devices. As database workloads make up a substantial fraction of the processing done by data centers, it is interesting to ask how switching to flash-based storage will affect the performance of database systems. We evaluate this question using IDE-based flash drives from two major manufacturers. We measure their read and write performance and find that flash has excellent random read performance, acceptable sequential read performance, and quite poor write performance compared to conventional IDE disks. We then consider how standard database algorithms are affected by these performance characteristics and find that the fast random read capability dramatically improves the performance of secondary indexes and index-based join algorithms. We next investigate using logstructured filesystems to mitigate the poor write performance of flash and find an 8.2x improvement in random write performance, but at the cost of a 3.7x decrease in random read performance. Finally, we study techniques for exploiting the inherent parallelism of multiple-chip flash devices, and we find that adaptive coding strategies can yield a 2x performance improvement over static ones. We conclude that in many cases flash disk performance is still worse than on traditional drives and that current flash technology may not yet be mature enough for widespread database adoption if performance is a dominant factor. Finally, we briefly speculate how this landscape may change based on expected performance of next-generation flash memories.by Daniel Myers.S.M

낸드 플래시 저장장치의 성능 및 수명 향상을 위한 프로그램 컨텍스트 기반 최적화 기법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 김지홍.컴퓨팅 시스템의 성능 향상을 위해, 기존의 느린 하드디스크(HDD)를 빠른 낸드 플래시 메모리 기반 저장장치(SSD)로 대체하고자 하는 연구가 최근 활발히 진행 되고 있다. 그러나 지속적인 반도체 공정 스케일링 및 멀티 레벨링 기술로 SSD 가격을 동급 HDD 수준으로 낮아졌지만, 최근의 첨단 디바이스 기술의 부작용으 로 NAND 플래시 메모리의 수명이 짧아지는 것은 고성능 컴퓨팅 시스템에서의 SSD의 광범위한 채택을 막는 주요 장벽 중 하나이다. 본 논문에서는 최근의 고밀도 낸드 플래시 메모리의 수명 및 성능 문제를 해결하기 위한 시스템 레벨의 개선 기술을 제안한다. 제안 된 기법은 응용 프로 그램의 쓰기 문맥을 활용하여 기존에는 얻을 수 없었던 데이터 수명 패턴 및 중복 데이터 패턴을 분석하였다. 이에 기반하여, 단일 계층의 단순한 정보만을 활용했 던 기존 기법의 한계를 극복함으로써 효과적으로 NAND 플래시 메모리의 성능 및 수명을 향상시키는 최적화 방법론을 제시한다. 먼저, 응용 프로그램의 I/O 작업에는 문맥에 따라 고유한 데이터 수명과 중 복 데이터의 패턴이 존재한다는 점을 분석을 통해 확인하였다. 문맥 정보를 효과 적으로 활용하기 위해 프로그램 컨텍스트 (쓰기 문맥) 추출 방법을 구현 하였다. 프로그램 컨텍스트 정보를 통해 가비지 컬렉션 부하와 제한된 수명의 NAND 플 래시 메모리 개선을 위한 기존 기술의 한계를 효과적으로 극복할 수 있다. 둘째, 멀티 스트림 SSD에서 WAF를 줄이기 위해 데이터 수명 예측의 정확 성을 높이는 기법을 제안하였다. 이를 위해 애플리케이션의 I/O 컨텍스트를 활용 하는 시스템 수준의 접근 방식을 제안하였다. 제안된 기법의 핵심 동기는 데이터 수명이 LBA보다 높은 추상화 수준에서 평가 되어야 한다는 것이다. 따라서 프 로그램 컨텍스트를 기반으로 데이터의 수명을 보다 정확히 예측함으로써, 기존 기법에서 LBA를 기반으로 데이터 수명을 관리하는 한계를 극복한다. 결론적으 로 따라서 가비지 컬렉션의 효율을 높이기 위해 수명이 짧은 데이터를 수명이 긴 데이터와 효과적으로 분리 할 수 있다. 마지막으로, 쓰기 프로그램 컨텍스트의 중복 데이터 패턴 분석을 기반으로 불필요한 중복 제거 작업을 피할 수있는 선택적 중복 제거를 제안한다. 중복 데 이터를 생성하지 않는 프로그램 컨텍스트가 존재함을 분석적으로 보이고 이들을 제외함으로써, 중복제거 동작의 효율성을 높일 수 있다. 또한 중복 데이터가 발생 하는 패턴에 기반하여 기록된 데이터를 관리하는 자료구조 유지 정책을 새롭게 제안하였다. 추가적으로, 서브 페이지 청크를 도입하여 중복 데이터를 제거 할 가능성을 높이는 세분화 된 중복 제거를 제안한다. 제안 된 기술의 효과를 평가하기 위해 다양한 실제 시스템에서 수집 된 I/O 트레이스에 기반한 시뮬레이션 평가 뿐만 아니라 에뮬레이터 구현을 통해 실제 응용을 동작하면서 일련의 평가를 수행했다. 더 나아가 멀티 스트림 디바이스의 내부 펌웨어를 수정하여 실제와 가장 비슷하게 설정된 환경에서 실험을 수행하 였다. 실험 결과를 통해 제안된 시스템 수준 최적화 기법이 성능 및 수명 개선 측면에서 기존 최적화 기법보다 더 효과적이었음을 확인하였다. 향후 제안된 기 법들이 보다 더 발전된다면, 낸드 플래시 메모리가 초고속 컴퓨팅 시스템의 주 저장장치로 널리 사용되는 데에 긍정적인 기여를 할 수 있을 것으로 기대된다.Replacing HDDs with NAND flash-based storage devices (SSDs) has been one of the major challenges in modern computing systems especially in regards to better performance and higher mobility. Although the continuous semiconductor process scaling and multi-leveling techniques lower the price of SSDs to the comparable level of HDDs, the decreasing lifetime of NAND flash memory, as a side effect of recent advanced device technologies, is emerging as one of the major barriers to the wide adoption of SSDs in highperformance computing systems. In this dissertation, system-level lifetime improvement techniques for recent high-density NAND flash memory are proposed. Unlike existing techniques, the proposed techniques resolve the problems of decreasing performance and lifetime of NAND flash memory by exploiting the I/O context of an application to analyze data lifetime patterns or duplicate data contents patterns. We first present that I/O activities of an application have distinct data lifetime and duplicate data patterns. In order to effectively utilize the context information, we implemented the program context extraction method. With the program context, we can overcome the limitations of existing techniques for improving the garbage collection overhead and limited lifetime of NAND flash memory. Second, we propose a system-level approach to reduce WAF that exploits the I/O context of an application to increase the data lifetime prediction for the multi-streamed SSDs. The key motivation behind the proposed technique was that data lifetimes should be estimated at a higher abstraction level than LBAs, so we employ a write program context as a stream management unit. Thus, it can effectively separate data with short lifetimes from data with long lifetimes to improve the efficiency of garbage collection. Lastly, we propose a selective deduplication that can avoid unnecessary deduplication work based on the duplicate data pattern analysis of write program context. With the help of selective deduplication, we also propose fine-grained deduplication which improves the likelihood of eliminating redundant data by introducing sub-page chunk. It also resolves technical difficulties caused by its finer granularity, i.e., increased memory requirement and read response time. In order to evaluate the effectiveness of the proposed techniques, we performed a series of evaluations using both a trace-driven simulator and emulator with I/O traces which were collected from various real-world systems. To understand the feasibility of the proposed techniques, we also implemented them in Linux kernel on top of our in-house flash storage prototype and then evaluated their effects on the lifetime while running real-world applications. Our experimental results show that system-level optimization techniques are more effective over existing optimization techniques.I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Garbage Collection Problem . . . . . . . . . . . . . 2 1.1.2 Limited Endurance Problem . . . . . . . . . . . . . 4 1.2 Dissertation Goals . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Dissertation Structure . . . . . . . . . . . . . . . . . . . . . 7 II. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 NAND Flash Memory System Software . . . . . . . . . . . 9 2.2 NAND Flash-Based Storage Devices . . . . . . . . . . . . . 10 2.3 Multi-stream Interface . . . . . . . . . . . . . . . . . . . . 11 2.4 Inline Data Deduplication Technique . . . . . . . . . . . . . 12 2.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5.1 Data Separation Techniques for Multi-streamed SSDs 13 2.5.2 Write Traffic Reduction Techniques . . . . . . . . . 15 2.5.3 Program Context based Optimization Techniques for Operating Systems . . . . . . . . 18 III. Program Context-based Analysis . . . . . . . . . . . . . . . . 21 3.1 Definition and Extraction of Program Context . . . . . . . . 21 3.2 Data Lifetime Patterns of I/O Activities . . . . . . . . . . . 24 3.3 Duplicate Data Patterns of I/O Activities . . . . . . . . . . . 26 IV. Fully Automatic Stream Management For Multi-Streamed SSDs Using Program Contexts . . 29 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2.1 No Automatic Stream Management for General I/O Workloads . . . . . . . . . 33 4.2.2 Limited Number of Supported Streams . . . . . . . 36 4.3 Automatic I/O Activity Management . . . . . . . . . . . . . 38 4.3.1 PC as a Unit of Lifetime Classification for General I/O Workloads . . . . . . . . . . . 39 4.4 Support for Large Number of Streams . . . . . . . . . . . . 41 4.4.1 PCs with Large Lifetime Variances . . . . . . . . . 42 4.4.2 Implementation of Internal Streams . . . . . . . . . 44 4.5 Design and Implementation of PCStream . . . . . . . . . . 46 4.5.1 PC Lifetime Management . . . . . . . . . . . . . . 46 4.5.2 Mapping PCs to SSD streams . . . . . . . . . . . . 49 4.5.3 Internal Stream Management . . . . . . . . . . . . . 50 4.5.4 PC Extraction for Indirect Writes . . . . . . . . . . 51 4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . 53 4.6.1 Experimental Settings . . . . . . . . . . . . . . . . 53 4.6.2 Performance Evaluation . . . . . . . . . . . . . . . 55 4.6.3 WAF Comparison . . . . . . . . . . . . . . . . . . . 56 4.6.4 Per-stream Lifetime Distribution Analysis . . . . . . 57 4.6.5 Impact of Internal Streams . . . . . . . . . . . . . . 58 4.6.6 Impact of the PC Attribute Table . . . . . . . . . . . 60 V. Deduplication Technique using Program Contexts . . . . . . 62 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.2 Selective Deduplication using Program Contexts . . . . . . . 63 5.2.1 PCDedup: Improving SSD Deduplication Efficiency using Selective Hash Cache Management . . . . . . 63 5.2.2 2-level LRU Eviction Policy . . . . . . . . . . . . . 68 5.3 Exploiting Small Chunk Size . . . . . . . . . . . . . . . . . 70 5.3.1 Fine-Grained Deduplication . . . . . . . . . . . . . 70 5.3.2 Read Overhead Management . . . . . . . . . . . . . 76 5.3.3 Memory Overhead Management . . . . . . . . . . . 80 5.3.4 Experimental Results . . . . . . . . . . . . . . . . . 82 VI. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 6.1 Summary and Conclusions . . . . . . . . . . . . . . . . . . 88 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 89 6.2.1 Supporting applications that have unusal program contexts . . . . . . . . . . . . . 89 6.2.2 Optimizing read request based on the I/O context . . 90 6.2.3 Exploiting context information to improve fingerprint lookups . . . . .. . . . . . 91 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Docto

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

PrismDB: Read-aware Log-structured Merge Trees for Heterogeneous Storage

In recent years, emerging hardware storage technologies have focused on divergent goals: better performance or lower cost-per-bit of storage. Correspondingly, data systems that employ these new technologies are optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by combining multiple tiers of fast and low-cost storage technologies within the same system, we can achieve a Pareto-efficient balance between performance and cost-per-bit. This paper presents the design and implementation of PrismDB, a novel log-structured merge tree based key-value store that exploits a full spectrum of heterogeneous storage technologies (from 3D XPoint to QLC NAND). We introduce the notion of "read-awareness" to log-structured merge trees, which allows hot objects to be pinned to faster storage, achieving better tiering and hot-cold separation of objects. Compared to the standard use of RocksDB on flash in datacenters today, PrismDB's average throughput on heterogeneous storage is 2.3$\times$ faster and its tail latency is more than an order of magnitude better, using hardware than is half the cost