Customized Interfaces for Modern Storage Devices

In the past decade, we have seen two major evolutions on storage technologies: flash storage and non-volatile memory. These storage technologies are both vastly different in their properties and implementations than the disk-based storage devices that current soft- ware stacks and applications have been built for and optimized over several decades. The second major trend that the industry has been witnessing is new classes of applications that are moving away from the conventional ACID (SQL) database access to storage. The resulting new class of NoSQL and in-memory storage applications consume storage using entirely new application programmer interfaces than their predecessors. The most significant outcome given these trends is that there is a great mismatch in terms of both application access interfaces and implementations of storage stacks when consuming these new technologies. In this work, we study the unique, intrinsic properties of current and next-generation storage technologies and propose new interfaces that allow application developers to get the most out of these storage technologies without having to become storage experts them- selves. We first build a new type of NoSQL key-value (KV) store that is FTL-aware rather than flash optimized. Our novel FTL cooperative design for KV store proofed to simplify development and outperformed state of the art KV stores, while reducing write amplification. Next, to address the growing relevance of byte-addressable persistent memory, we build a new type of KV store that is customized and optimized for persistent memory. The resulting KV store illustrates how to program persistent effectively while exposing a simpler interface and performing better than more general solutions. As the final component of the thesis, we build a generic, native storage solution for byte-addressable persistent memory. This new solution provides the most generic interface to applications, allow- ing applications to store and manipulate arbitrarily structured data with strong durability and consistency properties. With this new solution, existing applications as well as new “green field” applications will get to experience native performance and interfaces that are customized for the next storage technology evolution

Exploiting intrinsic flash properties to enhance modern storage systems

The longstanding goals of storage system design have been to provide simple abstractions for applications to efficiently access data while ensuring the data durability and security on a hardware device. The traditional storage system, which was designed for slow hard disk drive with little parallelism, does not fit for the new storage technologies such as the faster flash memory with high internal parallelism. The gap between the storage system software and flash device causes both resource inefficiency and sub-optimal performance. This dissertation focuses on the rethinking of the storage system design for flash memory with a holistic approach from the system level to the device level and revisits several critical aspects of the storage system design including the storage performance, performance isolation, energy-efficiency, and data security. The traditional storage system lacks full performance isolation between applications sharing the device because it does not make the software aware of the underlying flash properties and constraints. This dissertation proposes FlashBlox, a storage virtualization system that utilizes flash parallelism to provide hardware isolation between applications by assigning them on dedicated chips. FlashBlox reduces the tail latency of storage operations dramatically compared with the existing software-based isolation techniques while achieving uniform lifetime for the flash device. As the underlying flash device latency is reduced significantly compared to the conventional hard disk drive, the storage software overhead has become the major bottleneck. This dissertation presents FlashMap, a holistic flash-based storage stack that combines memory, storage and device-level indirections into a unified layer. By combining these layers, FlashMap reduces critical-path latency for accessing data in the flash device and improves DRAM caching efficiency significantly for flash management. The traditional storage software incurs energy-intensive storage operations due to the need for maintaining data durability and security for personal data, which has become a significant challenge for resource-constrained devices such as mobiles and wearables. This dissertation proposes WearDrive, a fast and energy-efficient storage system for wearables. WearDrive treats the battery-backed DRAM as non-volatile memory to store personal data and trades the connected phone’s battery for the wearable’s by performing large and energy-intensive tasks on the phone while performing small and energy-efficient tasks locally using battery-backed DRAM. WearDrive improves wearable’s battery life significantly with negligible impact to the phone’s battery life. The storage software which has been developed for decades is still vulnerable to malware attacks. For example, the encryption ransomware which is a malicious software that stealthily encrypts user files and demands a ransom to provide access to these files. Prior solutions such as ransomware detection and data backups have been proposed to defend against encryption ransomware. Unfortunately, by the time the ransomware is detected, some files already undergo encryption and the user is still required to pay a ransom to access those files. Furthermore, ransomware variants can obtain kernel privilege to terminate or destroy these software-based defense systems. This dissertation presents FlashGuard, a ransomware-tolerant SSD which has a firmware-level recovery system that allows effective data recovery from encryption ransomware. FlashGuard leverages the intrinsic flash properties to defend against the encryption ransomware and adds minimal overhead to regular storage operations.Ph.D

Flash memory management with cooperation, adaptation and assistance

Ph.DDOCTOR OF PHILOSOPH

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

TACKLING PERFORMANCE AND SECURITY ISSUES FOR CLOUD STORAGE SYSTEMS

Building data-intensive applications and emerging computing paradigm (e.g., Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT) in cloud computing environments is becoming a norm, given the many advantages in scalability, reliability, security and performance. However, under rapid changes in applications, system middleware and underlying storage device, service providers are facing new challenges to deliver performance and security isolation in the context of shared resources among multiple tenants. The gap between the decades-old storage abstraction and modern storage device keeps widening, calling for software/hardware co-designs to approach more effective performance and security protocols. This dissertation rethinks the storage subsystem from device-level to system-level and proposes new designs at different levels to tackle performance and security issues for cloud storage systems. In the first part, we present an event-based SSD (Solid State Drive) simulator that models modern protocols, firmware and storage backend in detail. The proposed simulator can capture the nuances of SSD internal states under various I/O workloads, which help researchers understand the impact of various SSD designs and workload characteristics on end-to-end performance. In the second part, we study the security challenges of shared in-storage computing infrastructures. Many cloud providers offer isolation at multiple levels to secure data and instance, however, security measures in emerging in-storage computing infrastructures are not studied. We first investigate the attacks that could be conducted by offloaded in-storage programs in a multi-tenancy cloud environment. To defend against these attacks, we build a lightweight Trusted Execution Environment, IceClave to enable security isolation between in-storage programs and internal flash management functions. We show that while enforcing security isolation in the SSD controller with minimal hardware cost, IceClave still keeps the performance benefit of in-storage computing by delivering up to 2.4x better performance than the conventional host-based trusted computing approach. In the third part, we investigate the performance interference problem caused by other tenants' I/O flows. We demonstrate that I/O resource sharing can often lead to performance degradation and instability. The block device abstraction fails to expose SSD parallelism and pass application requirements. To this end, we propose a software/hardware co-design to enforce performance isolation by bridging the semantic gap. Our design can significantly improve QoS (Quality of Service) by reducing throughput penalties and tail latency spikes. Lastly, we explore more effective I/O control to address contention in the storage software stack. We illustrate that the state-of-the-art resource control mechanism, Linux cgroups is insufficient for controlling I/O resources. Inappropriate cgroup configurations may even hurt the performance of co-located workloads under memory intensive scenarios. We add kernel support for limiting page cache usage per cgroup and achieving I/O proportionality

Data-intensive Systems on Modern Hardware : Leveraging Near-Data Processing to Counter the Growth of Data

Over the last decades, a tremendous change toward using information technology in almost every daily routine of our lives can be perceived in our society, entailing an incredible growth of data collected day-by-day on Web, IoT, and AI applications. At the same time, magneto-mechanical HDDs are being replaced by semiconductor storage such as SSDs, equipped with modern Non-Volatile Memories, like Flash, which yield significantly faster access latencies and higher levels of parallelism. Likewise, the execution speed of processing units increased considerably as nowadays server architectures comprise up to multiple hundreds of independently working CPU cores along with a variety of specialized computing co-processors such as GPUs or FPGAs. However, the burden of moving the continuously growing data to the best fitting processing unit is inherently linked to today’s computer architecture that is based on the data-to-code paradigm. In the light of Amdahl's Law, this leads to the conclusion that even with today's powerful processing units, the speedup of systems is limited since the fraction of parallel work is largely I/O-bound. Therefore, throughout this cumulative dissertation, we investigate the paradigm shift toward code-to-data, formally known as Near-Data Processing (NDP), which relieves the contention on the I/O bus by offloading processing to intelligent computational storage devices, where the data is originally located. Firstly, we identified Native Storage Management as the essential foundation for NDP due to its direct control of physical storage management within the database. Upon this, the interface is extended to propagate address mapping information and to invoke NDP functionality on the storage device. As the former can become very large, we introduce Physical Page Pointers as one novel NDP abstraction for self-contained immutable database objects. Secondly, the on-device navigation and interpretation of data are elaborated. Therefore, we introduce cross-layer Parsers and Accessors as another NDP abstraction that can be executed on the heterogeneous processing capabilities of modern computational storage devices. Thereby, the compute placement and resource configuration per NDP request is identified as a major performance criteria. Our experimental evaluation shows an improvement in the execution durations of 1.4x to 2.7x compared to traditional systems. Moreover, we propose a framework for the automatic generation of Parsers and Accessors on FPGAs to ease their application in NDP. Thirdly, we investigate the interplay of NDP and modern workload characteristics like HTAP. Therefore, we present different offloading models and focus on an intervention-free execution. By propagating the Shared State with the latest modifications of the database to the computational storage device, it is able to process data with transactional guarantees. Thus, we achieve to extend the design space of HTAP with NDP by providing a solution that optimizes for performance isolation, data freshness, and the reduction of data transfers. In contrast to traditional systems, we experience no significant drop in performance when an OLAP query is invoked but a steady and 30% faster throughput. Lastly, in-situ result-set management and consumption as well as NDP pipelines are proposed to achieve flexibility in processing data on heterogeneous hardware. As those produce final and intermediary results, we continue investigating their management and identified that an on-device materialization comes at a low cost but enables novel consumption modes and reuse semantics. Thereby, we achieve significant performance improvements of up to 400x by reusing once materialized results multiple times

Garbage Collection Techniques for Flash-Resident Page-Mapping FTLs

Storage devices based on flash memory have replaced hard disk drives (HDDs) due to their superior performance, increasing density, and lower power consumption. Unfortunately, flash memory is subject to challenging idiosyncrasies like erase-before-write and limited block lifetime. These constraints are handled by a flash translation layer (FTL), which performs out-of-place updates, wear-leveling and garbage-collection behind the scene, while offering the application a virtualization of the physical address space. A class of relevant FTLs employ a flash-resident page-associative mapping table from logical to physical addresses, with a smaller RAM-resident cache for frequently mapped entries. In this paper, we address the problem of performing garbage-collection under such FTLs. We observe two problems. Firstly, maintaining the metadata needed to perform garbage-collection under these schemes is problematic, because at write-time we do not necessarily know the physical address of the before-image. Secondly, the size of this metadata must remain small, because it makes RAM unavailable for caching frequently accessed entries. We propose two complementary techniques, called Lazy Gecko and Logarithmic Gecko, which address these issues. Lazy Gecko works well when RAM is plentiful enough to store the GC metadata. Logarithmic Gecko works well when RAM isn't plentiful and efficiently stores the GC metadata in flash. Thus, these techniques are applicable to a wide range of flash devices with varying amounts of embedded RAM