Design of Swap Space in Virtual Memory Management System

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Elevating commodity storage with the SALSA host translation layer

To satisfy increasing storage demands in both capacity and performance, industry has turned to multiple storage technologies, including Flash SSDs and SMR disks. These devices employ a translation layer that conceals the idiosyncrasies of their mediums and enables random access. Device translation layers are, however, inherently constrained: resources on the drive are scarce, they cannot be adapted to application requirements, and lack visibility across multiple devices. As a result, performance and durability of many storage devices is severely degraded. In this paper, we present SALSA: a translation layer that executes on the host and allows unmodified applications to better utilize commodity storage. SALSA supports a wide range of single- and multi-device optimizations and, because is implemented in software, can adapt to specific workloads. We describe SALSA's design, and demonstrate its significant benefits using microbenchmarks and case studies based on three applications: MySQL, the Swift object store, and a video server.Comment: Presented at 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

Synergistically Coupling Of Solid State Drives And Hard Disks For Qos-Aware Virtual Memory

With significant advantages in capacity, power consumption, and price, solid state disk (SSD) has good potential to be employed as an extension of dynamic random-access memory, such that applications with large working sets could run efficiently on a modestly configured system. While initial results reported in recent works show promising prospects for this use of SSD by incorporating it into the management of virtual memory, frequent writes from write-intensive programs could quickly wear out SSD, making the idea less practical. This thesis makes four contributions towards solving this issue. First, we propose a scheme, HybridSwap, that integrates a hard disk with an SSD for virtual memory man-agement, synergistically achieving the advantages of both. In addition, HybridSwap can constrain performance loss caused by swapping according to user-specified QoS requirements. Second, We develop an efficient algorithm to record memory access history and to identify page access sequences and evaluate their locality. Using a history of page access patterns HybridSwap dynamically creates an out-of-memory virtual memory page layout on the swap space spanning the SSD and hard disk such that random reads are served by SSD and sequential reads are asynchronously served by the hard disk with high efficiency. Third, we build a QoS-assurance mechanism into HybridSwap to demonstrate the flexibility of the system in bounding the performance penalty due to swapping. It allows users to specify a bound on the program stall time due to page faults as a percentage of the program\u27s total run time. Forth, we have implemented HybridSwap in a recent Linux kernel, version 2.6.35.7. Our evaluation with representative benchmarks, such as Memcached for key-value store, and scientific programs from the ALGLIB cross-platform numerical analysis and data processing library, shows that the number of writes to SSD can be reduced by 40% with the system\u27s performance comparable to that with pure SSD swapping, and can satisfy a swapping-related QoS requirement as long as the I/O resource is sufficient

RackBlox: A Software-Defined Rack-Scale Storage System with Network-Storage Co-Design

Software-defined networking (SDN) and software-defined flash (SDF) have been serving as the backbone of modern data centers. They are managed separately to handle I/O requests. At first glance, this is a reasonable design by following the rack-scale hierarchical design principles. However, it suffers from suboptimal end-to-end performance, due to the lack of coordination between SDN and SDF. In this paper, we co-design the SDN and SDF stack by redefining the functions of their control plane and data plane, and splitting up them within a new architecture named RackBlox. RackBlox decouples the storage management functions of flash-based solid-state drives (SSDs), and allow the SDN to track and manage the states of SSDs in a rack. Therefore, we can enable the state sharing between SDN and SDF, and facilitate global storage resource management. RackBlox has three major components: (1) coordinated I/O scheduling, in which it dynamically adjusts the I/O scheduling in the storage stack with the measured and predicted network latency, such that it can coordinate the effort of I/O scheduling across the network and storage stack for achieving predictable end-to-end performance; (2) coordinated garbage collection (GC), in which it will coordinate the GC activities across the SSDs in a rack to minimize their impact on incoming I/O requests; (3) rack-scale wear leveling, in which it enables global wear leveling among SSDs in a rack by periodically swapping data, for achieving improved device lifetime for the entire rack. We implement RackBlox using programmable SSDs and switch. Our experiments demonstrate that RackBlox can reduce the tail latency of I/O requests by up to 5.8x over state-of-the-art rack-scale storage systems.Comment: 14 pages. Published in published in ACM SIGOPS 29th Symposium on Operating Systems Principles (SOSP'23

메모리 스왑 패턴 분석을 통한 스왑 시스템 최적화

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 염헌영.The use of memory is one of the key parts of modern computer architecture (Von Neumann architecture) but when considering limited memory, it could be the most lethal part at the same time. Advances in hardware and software are making rapid strides in areas such as Big Data, HPC and machine learning and facing new turning points, while the use of memory increases along with those advances. In the server environment, various programs share resources which leads to a shortage of resources. Memory is one of those resources and needs to be managed. When the system is out of memory, the operating system evicts some of the pages out to storage and then loads the requested pages in memory. Given that the storage performance is slower than the memory, swap-induced delay is one of the critical issues in the overall performance degradation. Therefore, we designed and implemented a swpTracer to provide visualization to trace the swap in/out movement. To check the generality of the tool, we used mlock to optimize 429.mcf of Spec CPU 2006 based on the hint from swpTracer. The optimized program executes 2 to 3 times faster than the original program in a memory scarce environment. The scope of the performance improvement with previous system calls decreases when the memory limit increases. To sustain the improvement, we build a swap- prefetch to read ahead the swapped-out pages. The optimized application with swpTracer and swap-prefetch consistently exceeds the performance of the original code by 1.5x.메모리의 사용은 현대 컴퓨터 아키텍처(폰 노이만 아키텍쳐)의 핵심 부분 중 하 나이기 때문에, 메모리가 부족한 환경은 성능에 치명적인다. 하드웨어와 소프트웨 어의 발전으로 빅데이터, HPC, 머신러닝과 같은 분야들이 빠른 속도로 발전하여 그에 따라 메모리의 사용량도 증가한다. 따라서, 메모리가 제한된 임베디드 환경 이나, 여러 작업이 동시에 수행되는 서버에서 메모리 부족으로 작업이 중단되는 문제가 발생한다. 시스템이메모리가부족하면운영체제는일부페이지를저장소로내보낸다음 요청된 페이지를 메모리에 로드한다. 스토리지 성능이 메모리보다 느리다는 점에 서 스왑에 의한 지연은 전반적인 성능 저하의 중요한 문제 중 하나이다. 따라서 스왑이 프로그램 수행 시간에 영향을 미치지 않도록 프로그램의 스왑 발생 추이를 분석하여 스왑 발생을 줄일 수 있도록 힌트를 주는 도구인 swpTracer를 설계, 실 행했다. mlock을 사용하여 Spec CPU 2006 벤치마크 중 429.mcf에 적용했을 때 기존 프로그램 대비 2, 3 배 성능이 빨라졌다. 기존의 시스템 콜을 사용하여 최적화했을 때 메모리가 살짝 부족한 경우에는 비슷한성능을보여주지만, 메모리가 50% 부족한순간부터성능향상폭이줄었다. 이를 보완하기 위해 스왑 아웃 되었던 페이지를 미리 읽어두는 swap-prefetch를 구현했다. 배열을 3번 횡단하는 프로그램을 대상으로 배열의 크기를 조절하면서 swap-prefetch의 성능을 시험했다. 원본 코드와 시스템 함수인 madvise를 사용 했을 때보다 평균적으로 1.5 좋아졌다. 또, swap-prefetch를 다른 시스템 함수를 사용했을 때와 mlock과 비교했을 때 평균 1.25배 성능이 빨라졌다.Abstract Chapter 1 Introduction 1 Chapter 2 Background 4 2.1 Page Reclamation Policy . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Linux Swap Management . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Linux System Calls . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 3 Design and Implementation 8 3.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2.1 Kernel Level . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2.2 Application Level . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 4 Evaluation 15 4.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4.2.1 Generality of swpTracer . . . . . . . . . . . . . . . . . . . 16 4.2.2 Memory Optimization Method Comparison . . . . . . . . 17 Chapter 5 Related Work 20 Chapter 6 Conclusion 22 Bibliography 초록 28Maste