데이터 집약적 응용의 효율적인 시스템 자원 활용을 위한 메모리 서브시스템 최적화

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2020. 8. 염헌영.With explosive data growth, data-intensive applications, such as relational database and key-value storage, have been increasingly popular in a variety of domains in recent years. To meet the growing performance demands of data-intensive applications, it is crucial to efficiently and fully utilize memory resources for the best possible performance. However, general-purpose operating systems (OSs) are designed to provide system resources to applications running on a system in a fair manner at system-level. A single application may find it difficult to fully exploit the systems best performance due to this system-level fairness. For performance reasons, many data-intensive applications implement their own mechanisms that OSs already provide, under the assumption that they know better about the data than OSs. They can be greedily optimized for performance but this may result in inefficient use of system resources. In this dissertation, we claim that simple OS support with minor application modifications can yield even higher application performance without sacrificing system-level resource utilization. We optimize and extend OS memory subsystem for better supporting applications while addressing three memory-related issues in data-intensive applications. First, we introduce a memory-efficient cooperative caching approach between application and kernel buffer to address double caching problem where the same data resides in multiple layers. Second, we present a memory-efficient, transparent zero-copy read I/O scheme to avoid the performance interference problem caused by memory copy behavior during I/O. Third, we propose a memory-efficient fork-based checkpointing mechanism for in-memory database systems to mitigate the memory footprint problem of the existing fork-based checkpointing scheme; memory usage increases incrementally (up to 2x) during checkpointing for update-intensive workloads. To show the effectiveness of our approach, we implement and evaluate our schemes on real multi-core systems. The experimental results demonstrate that our cooperative approach can more effectively address the above issues related to data-intensive applications than existing non-cooperative approaches while delivering better performance (in terms of transaction processing speed, I/O throughput, or memory footprint).최근 폭발적인 데이터 성장과 더불어 데이터베이스, 키-밸류 스토리지 등의 데이터 집약적인 응용들이 다양한 도메인에서 인기를 얻고 있다. 데이터 집약적인 응용의 높은 성능 요구를 충족하기 위해서는 주어진 메모리 자원을 효율적이고 완벽하게 활용하는 것이 중요하다. 그러나, 범용 운영체제(OS)는 시스템에서 수행 중인 모든 응용들에 대해 시스템 차원에서 공평하게 자원을 제공하는 것을 우선하도록 설계되어있다. 즉, 시스템 차원의 공평성 유지를 위한 운영체제 지원의 한계로 인해 단일 응용은 시스템의 최고 성능을 완전히 활용하기 어렵다. 이러한 이유로, 많은 데이터 집약적 응용은 운영체제에서 제공하는 기능에 의지하지 않고 비슷한 기능을 응용 레벨에 구현하곤 한다. 이러한 접근 방법은 탐욕적인 최적화가 가능하다는 점에서 성능 상 이득이 있을 수 있지만, 시스템 자원의 비효율적인 사용을 초래할 수 있다. 본 논문에서는 운영체제의 지원과 약간의 응용 수정만으로도 비효율적인 시스템 자원 사용 없이 보다 높은 응용 성능을 보일 수 있음을 증명하고자 한다. 그러기 위해 운영체제의 메모리 서브시스템을 최적화 및 확장하여 데이터 집약적인 응용에서 발생하는 세 가지 메모리 관련 문제를 해결하였다. 첫째, 동일한 데이터가 여러 계층에 존재하는 중복 캐싱 문제를 해결하기 위해 응용과 커널 버퍼 간에 메모리 효율적인 협력 캐싱 방식을 제시하였다. 둘째, 입출력시 발생하는 메모리 복사로 인한 성능 간섭 문제를 피하기 위해 메모리 효율적인 무복사 읽기 입출력 방식을 제시하였다. 셋째, 인-메모리 데이터베이스 시스템을 위한 메모리 효율적인 fork 기반 체크포인트 기법을 제안하여 기존 포크 기반 체크포인트 기법에서 발생하는 메모리 사용량 증가 문제를 완화하였다; 기존 방식은 업데이트 집약적 워크로드에 대해 체크포인팅을 수행하는 동안 메모리 사용량이 최대 2배까지 점진적으로 증가할 수 있었다. 본 논문에서는 제안한 방법들의 효과를 증명하기 위해 실제 멀티 코어 시스템에 구현하고 그 성능을 평가하였다. 실험결과를 통해 제안한 협력적 접근방식이 기존의 비협력적 접근방식보다 데이터 집약적 응용에게 효율적인 메모리 자원 활용을 가능하게 함으로써 더 높은 성능을 제공할 수 있음을 확인할 수 있었다.Chapter 1 Introduction 1 1.1 Motivation 1 1.1.1 Importance of Memory Resources 1 1.1.2 Problems 2 1.2 Contributions 5 1.3 Outline 6 Chapter 2 Background 7 2.1 Linux Kernel Memory Management 7 2.1.1 Page Cache 7 2.1.2 Page Reclamation 8 2.1.3 Page Table and TLB Shootdown 9 2.1.4 Copy-on-Write 10 2.2 Linux Support for Applications 11 2.2.1 fork 11 2.2.2 madvise 11 2.2.3 Direct I/O 12 2.2.4 mmap 13 Chapter 3 Memory Efficient Cooperative Caching 14 3.1 Motivation 14 3.1.1 Problems of Existing Datastore Architecture 14 3.1.2 Proposed Architecture 17 3.2 Related Work 17 3.3 Design and Implementation 19 3.3.1 Overview 19 3.3.2 Kernel Support 24 3.3.3 Migration to DBIO 25 3.4 Evaluation 27 3.4.1 System Configuration 27 3.4.2 Methodology 28 3.4.3 TPC-C Benchmarks 30 3.4.4 YCSB Benchmarks 32 3.5 Summary 37 Chapter 4 Memory Efficient Zero-copy I/O 38 4.1 Motivation 38 4.1.1 The Problems of Copy-Based I/O 38 4.2 Related Work 40 4.2.1 Zero Copy I/O 40 4.2.2 TLB Shootdown 42 4.2.3 Copy-on-Write 43 4.3 Design and Implementation 44 4.3.1 Prerequisites for z-READ 44 4.3.2 Overview of z-READ 45 4.3.3 TLB Shootdown Optimization 48 4.3.4 Copy-on-Write Optimization 52 4.3.5 Implementation 55 4.4 Evaluation 55 4.4.1 System Configurations 56 4.4.2 Effectiveness of the TLB Shootdown Optimization 57 4.4.3 Effectiveness of CoW Optimization 59 4.4.4 Analysis of the Performance Improvement 62 4.4.5 Performance Interference Intensity 63 4.4.6 Effectiveness of z-READ in Macrobenchmarks 65 4.5 Summary 67 Chapter 5 Memory Efficient Fork-based Checkpointing 69 5.1 Motivation 69 5.1.1 Fork-based Checkpointing 69 5.1.2 Approach 71 5.2 Related Work 73 5.3 Design and Implementation 74 5.3.1 Overview 74 5.3.2 OS Support 78 5.3.3 Implementation 79 5.4 Evaluation 80 5.4.1 Experimental Setup 80 5.4.2 Performance 81 5.5 Summary 86 Chapter 6 Conclusion 87 요약 100Docto

An efficient storage manager

When dealing with large quantities of clauses, the use of persistent knowledge is inevitable, and indexing methods are essential to answer queries efficiently. We introduce PerKMan, a storage manager that uses G-trees and aims at efficient manipulation of large amount of persistent knowledge. PerKMan may be connected to Prolog systems that offer an external C language interface. As well as the fact that the storage manager allows different arguments of a predicate to share a common index dimension in a novel manner, it indexes rules and facts in the same manner. PerKMan handles compound terms efficiently and its data structures adapt their shape to large dynamic volumes of clauses, no matter what their distribution. The storage manager achieves fast clause retrieval and reasonable use of disk space. © Springer-Verlag Berlin Heidelberg 2000

Thresher: An efficient storage manager for copy-on-write snapshots

A new generation of storage systems exploit decreasing storage costs to allow applications to take snapshots of past states and retain them for long durations. Over time, current snapshot techniques can produce large volumes of snapshots. Indiscriminately keeping all snapshots accessible is impractical, even if raw disk storage is cheap, because administering such large-volume storage is expensive over a long duration. Moreover, not all snapshots are equally valuable. Thresher is a new snapshot storage management system, based on novel copyon-write snapshot techniques, that is the first to provide applications the ability to discriminate among snapshots efficiently. Valuable snapshots can remain accessible or stored with faster access while less valuable snapshots are discarded or moved off-line. Measurements of the Thresher prototype indicate that the new techniques are efficient and scalable, imposing minimal (4%) performance penalty on expected common workloads.

Data Models, Query Execution, and Storage for Traditional & Immersive Video Data Management

Thesis (Ph.D.)--University of Washington, 2020The proliferation of cameras deployed throughout our world is enabling and accelerating exciting new applications such as virtual and augmented reality (VR and AR), autonomous driving, drone analytics, and smart infrastructure. However, these cameras collectively produce staggering quantities of video data. VR spherical (360°) video is up to 20x larger in size than its 2D counterparts. Closed-circuit television camera networks, consisting of tens of thousands of cameras, generate petabytes of video data per day. A single autonomous vehicle can generate tens of terabytes of video data per hour. Due to these massive data sizes and the complexity involved with reasoning about large numbers of cameras, developing applications that use real world video data remains challenging. Developers must be cognizant of the low-level storage intricacies of video formats and compression. They need expertise in device-specific programming (e.g., GPUs), and, to maximize performance, they must be able to balance execution across heterogeneous, possibly distributed hardware. In this thesis, we describe several video data management systems designed to simplify application development, optimize execution, evaluate performance, and move forward the state of the art in video data management. The first system, LightDB, presents a simple, declarative interface for VR and AR video application development. It implements powerful query optimization techniques, an efficient storage manager, and a suite of novel physical optimizations. To further improve the performance of video applications, we next introduce a new video file system (VFS), which can serve as a storage manager for video data management systems (such as LightDB and others) or can be used as a standalone system. It is designed to decouple video application design from a video's underlying physical layout and compressed format. Finally, analogous to standardized benchmarks for other areas of data management research, we develop a new benchmark--Visual Road--aimed specifically at evaluating the performance and scalability of video-oriented data management systems and frameworks. By exposing declarative interfaces, LightDB and VFS automatically produce efficient execution strategies that include leveraging heterogeneous hardware, operating directly on the compressed representation of video data, and improving video storage performance. Visual Road reproducibly and objectively measures how well a video system or framework executes a battery of microbenchmarks real-world video-oriented workloads. Collectively through these systems, we show that the application of fundamental data management principles to this space vastly improves runtime performance (by up to 500x), storage performance (up to 45% decrease in file sizes), and greatly decreases application development complexity (decreasing lines of code by up to 90%)