Flashcache: a NAND Flash Memory File Cache for Low Power Web Servers
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Copy on Write Based File Systems Performance Analysis and Implementation
Copy On Write Based File Systems Performance Analysis And Implementation Sakis Kasampalis Kongens Lyngby 2010 IMM-MSC-2010-63 Technical University of Denmark Department Of Informatics Building 321, DK-2800 Kongens Lyngby, Denmark Phone +45 45253351, Fax +45 45882673 [email protected] www.imm.dtu.dk Abstract In this work I am focusing on Copy On Write based file systems. Copy On Write is used on modern file systems for providing (1) metadata and data consistency using transactional semantics, (2) cheap and instant backups using snapshots and clones. This thesis is divided into two main parts. The first part focuses on the design and performance of Copy On Write based file systems. Recent efforts aiming at creating a Copy On Write based file system are ZFS, Btrfs, ext3cow, Hammer, and LLFS. My work focuses only on ZFS and Btrfs, since they support the most advanced features. The main goals of ZFS and Btrfs are to offer a scalable, fault tolerant, and easy to administrate file system. I evaluate the performance and scalability of ZFS and Btrfs. The evaluation includes studying their design and testing their performance and scalability against a set of recommended file system benchmarks. Most computers are already based on multi-core and multiple processor architec- tures. Because of that, the need for using concurrent programming models has increased. Transactions can be very helpful for supporting concurrent program- ming models, which ensure that system updates are consistent. Unfortunately, the majority of operating systems and file systems either do not support trans- actions at all, or they simply do not expose them to the users. -
The Page Cache Today’S Lecture RCU File System Networking(Kernel Level Syncmem
2/21/20 COMP 790: OS Implementation COMP 790: OS Implementation Logical Diagram Binary Memory Threads Formats Allocators User System Calls Kernel The Page Cache Today’s Lecture RCU File System Networking(kernel level Syncmem. Don Porter management) Memory Device CPU Management Drivers Scheduler Hardware Interrupts Disk Net Consistency 1 2 1 2 COMP 790: OS Implementation COMP 790: OS Implementation Recap of previous lectures Background • Page tables: translate virtual addresses to physical • Lab2: Track physical pages with an array of PageInfo addresses structs • VM Areas (Linux): track what should be mapped at in – Contains reference counts the virtual address space of a process – Free list layered over this array • Hoard/Linux slab: Efficient allocation of objects from • Just like JOS, Linux represents physical memory with a superblock/slab of pages an array of page structs – Obviously, not the exact same contents, but same idea • Pages can be allocated to processes, or to cache file data in memory 3 4 3 4 COMP 790: OS Implementation COMP 790: OS Implementation Today’s Problem The address space abstraction • Given a VMA or a file’s inode, how do I figure out • Unifying abstraction: which physical pages are storing its data? – Each file inode has an address space (0—file size) • Next lecture: We will go the other way, from a – So do block devices that cache data in RAM (0---dev size) physical page back to the VMA or file inode – The (anonymous) virtual memory of a process has an address space (0—4GB on x86) • In other words, all page -
Advanced NAND Flash Memory Single-Chip Storage Solution
SOLUTION SHEET: ADVANCED SOLID STATE MEMORY CONTROLLER Advanced NAND Flash Memory Single-Chip Storage Solution ? The growing complexity of flash memory management algorithms has made controller designs complex and has impacted the performance and the diversity of NAND devices that a single controller can support. As the complexity of the controller design increases, there comes a point where hardware acceleration is necessary. We see this trend for partial FTL (Flash Translation Layer) layers, addressing and wear leveling, and command queuing. At the same time, algorithms and flash device characteristics constantly change through the life of the controller. But data center managers expect the controller to constantly make design tradeoffs in order to improve in-system performance. These changes are also impacted by the traffic patterns of the application. The challenge is to provide hardware-accelerated algorithms that can change frequently in the field. The Context Solid-state storage based on NAND flash technology has become an important new lever for improving data-center performance and predictability. But the solid-state drives also present cost and predictability issues of their own. Surprisingly, these issues can be addressed through design of the flash memory controller. The NAND array has become the dominating factor for the cost of the drive. Increasing the life expectancy of the NAND array obviously reduces the total cost of ownership of the system. Also, the ability to reconfigure the controller to implement improved algorithms or to adapt to a new generation of flash chips extends the system life. Performance predictability and throughput are key concerns for data-center managers. -
Dynamically Tuning the JFS Cache for Your Job Sjoerd Visser Dynamically Tuning the JFS Cache for Your Job Sjoerd Visser
Dynamically Tuning the JFS Cache for Your Job Sjoerd Visser Dynamically Tuning the JFS Cache for Your Job Sjoerd Visser The purpose of this presentation is the explanation of: IBM JFS goals: Where was Journaled File System (JFS) designed for? JFS cache design: How the JFS File System and Cache work. JFS benchmarking: How to measure JFS performance under OS/2. JFS cache tuning: How to optimize JFS performance for your job. What do these settings say to you? [E:\]cachejfs SyncTime: 8 seconds MaxAge: 30 seconds BufferIdle: 6 seconds Cache Size: 400000 kbytes Min Free buffers: 8000 ( 32000 K) Max Free buffers: 16000 ( 64000 K) Lazy Write is enabled Do you have a feeling for this? Do you understand the dynamic cache behaviour of the JFS cache? Or do you just rely on the “proven” cachejfs settings that the eCS installation presented to you? Do you realise that the JFS cache behaviour may be optimized for your jobs? November 13, 2009 / page 2 Dynamically Tuning the JFS Cache for Your Job Sjoerd Visser Where was Journaled File System (JFS) designed for? 1986 Advanced Interactive eXecutive (AIX) v.1 based on UNIX System V. for IBM's RT/PC. 1990 JFS1 on AIX was introduced with AIX version 3.1 for the RS/6000 workstations and servers using 32-bit and later 64-bit IBM POWER or PowerPC RISC CPUs. 1994 JFS1 was adapted for SMP servers (AIX 4) with more CPU power, many hard disks and plenty of RAM for cache and buffers. 1995-2000 JFS(2) (revised AIX independent version in c) was ported to OS/2 4.5 (1999) and Linux (2000) and also was the base code of the current JFS2 on AIX branch. -
NOVA: a Log-Structured File System for Hybrid Volatile/Non
NOVA: A Log-structured File System for Hybrid Volatile/Non-volatile Main Memories Jian Xu and Steven Swanson, University of California, San Diego https://www.usenix.org/conference/fast16/technical-sessions/presentation/xu This paper is included in the Proceedings of the 14th USENIX Conference on File and Storage Technologies (FAST ’16). February 22–25, 2016 • Santa Clara, CA, USA ISBN 978-1-931971-28-7 Open access to the Proceedings of the 14th USENIX Conference on File and Storage Technologies is sponsored by USENIX NOVA: A Log-structured File System for Hybrid Volatile/Non-volatile Main Memories Jian Xu Steven Swanson University of California, San Diego Abstract Hybrid DRAM/NVMM storage systems present a host of opportunities and challenges for system designers. These sys- Fast non-volatile memories (NVMs) will soon appear on tems need to minimize software overhead if they are to fully the processor memory bus alongside DRAM. The result- exploit NVMM’s high performance and efficiently support ing hybrid memory systems will provide software with sub- more flexible access patterns, and at the same time they must microsecond, high-bandwidth access to persistent data, but provide the strong consistency guarantees that applications managing, accessing, and maintaining consistency for data require and respect the limitations of emerging memories stored in NVM raises a host of challenges. Existing file sys- (e.g., limited program cycles). tems built for spinning or solid-state disks introduce software Conventional file systems are not suitable for hybrid mem- overheads that would obscure the performance that NVMs ory systems because they are built for the performance char- should provide, but proposed file systems for NVMs either in- acteristics of disks (spinning or solid state) and rely on disks’ cur similar overheads or fail to provide the strong consistency consistency guarantees (e.g., that sector updates are atomic) guarantees that applications require. -
Using Data Postcompensation and Predistortion to Tolerate Cell-To-Cell
2718 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: REGULAR PAPERS, VOL. 57, NO. 10, OCTOBER 2010 Using Data Postcompensation and Predistortion to Tolerate Cell-to-Cell Interference in MLC NAND Flash Memory Guiqiang Dong, Student Member, IEEE, Shu Li, and Tong Zhang, Senior Member, IEEE Abstract—With the appealing storage-density advantage, mul- than 1 bit in each memory cell (or floating-gate MOS tran- tilevel-per-cell (MLC) NAND Flash memory that stores more than sistor) by programming the cell threshold voltage into one 1 bit in each memory cell now largely dominates the global Flash of voltage windows, has been widely used to further memory market. However, due to the inherent smaller noise NAND margin, the MLC NAND Flash memory is more subject to various improve the Flash memory storage density. Because of device/circuit variability and noise, particularly as the industry is its obvious advantages in terms of storage density and, hence, pushing the limit of technology scaling and a more aggressive use cost, MLC NAND Flash memory now largely dominates the of MLC storage. Cell-to-cell interference has been well recognized global Flash memory market. In current design practice, most as a major noise source responsible for raw-memory-storage MLC NAND Flash memories store 2 bits per cell, while 3- and reliability degradation. Leveraging the fact that cell-to-cell in- terference is a deterministic data-dependent process and can even 4-bit-per-cell NAND Flash memories have been recently be mathematically described with a simple formula, we present reported in the open literature [2]–[6]. -
Hibachi: a Cooperative Hybrid Cache with NVRAM and DRAM for Storage Arrays
Hibachi: A Cooperative Hybrid Cache with NVRAM and DRAM for Storage Arrays Ziqi Fan, Fenggang Wu, Dongchul Parkx, Jim Diehl, Doug Voigty, and David H.C. Du University of Minnesota–Twin Cities, xIntel Corporation, yHewlett Packard Enterprise Email: [email protected], ffenggang, park, [email protected], [email protected], [email protected] Abstract—Adopting newer non-volatile memory (NVRAM) Application Application Application technologies as write buffers in slower storage arrays is a promising approach to improve write performance. However, due OS … OS … OS to DRAM’s merits, including shorter access latency and lower Buffer/Cache Buffer/Cache Buffer/Cache cost, it is still desirable to incorporate DRAM as a read cache along with NVRAM. Although numerous cache policies have been Application, web or proposed, most are either targeted at main memory buffer caches, database servers or manage NVRAM as write buffers and separately manage DRAM as read caches. To the best of our knowledge, cooperative hybrid volatile and non-volatile memory buffer cache policies specifically designed for storage systems using newer NVRAM Storage Area technologies have not been well studied. Network (SAN) This paper, based on our elaborate study of storage server Hibachi Cache block I/O traces, proposes a novel cooperative HybrId NVRAM DRAM NVRAM and DRAM Buffer cACHe polIcy for storage arrays, named Hibachi. Hibachi treats read cache hits and write cache hits differently to maximize cache hit rates and judiciously adjusts the clean and the dirty cache sizes to capture workloads’ tendencies. In addition, it converts random writes to sequential writes for high disk write throughput and further exploits storage server I/O workload characteristics to improve read performance. -
Speeding Data Access with the Sun Flash Accelerator F20 Pcie Card
An Oracle White Paper June 2010 Speeding Data Access with the Sun Flash Accelerator F20 PCIe Card Speeding Data Access with the Sun Flash Accelerator F20 PCIe Card Introduction..........................................................................................1 Accelerating Application Performance with Enterprise-Quality Sun FlashFire Technology ..........................................................................................3 Sun Flash Accelerator F20 PCIe Card Highlights ...........................4 Sun Flash Accelerator F20 PCIe Card Architecture ............................5 Sun Flash Accelerator F20 PCIe Card Components...........................5 Disk on Module Design....................................................................6 Enterprise-Quality NAND Flash for Reliability........................................ 8 Serial-Attached SCSI Controller ......................................................8 Twelve-Port Serial-Attached SCSI Expander ..................................8 Supercapacitor Module....................................................................8 Supercapacitors versus Batteries .......................................................... 8 Reliability, Availability, and Serviceability Features.............................9 Deploying the Sun Flash Accelerator F20 PCIe Card .......................11 Flash Technology Considerations .................................................11 Aligning Data Transfers and Block Size............................................... 12 Database Acceleration -
Data Storage on Unix
Data Storage on Unix Patrick Louis 2017-11-05 Published online on venam.nixers.net © Patrick Louis 2017 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of the rightful author. First published eBook format 2017 The author has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Contents Introduction 4 Ideas and Concepts 5 The Overall Generic Architecture 6 Lowest Level - Hardware & Limitation 8 The Medium ................................ 8 Connectors ................................. 10 The Drivers ................................. 14 A Mention on Block Devices and the Block Layer ......... 16 Mid Level - Partitions and Volumes Organisation 18 What’s a partitions ............................. 21 High Level 24 A Big Overview of FS ........................... 24 FS Examples ............................. 27 A Bit About History and the Origin of Unix FS .......... 28 VFS & POSIX I/O Layer ...................... 28 POSIX I/O 31 Management, Commands, & Forensic 32 Conclusion 33 Bibliography 34 3 Introduction Libraries and banks, amongst other institutions, used to have a filing system, some still have them. They had drawers, holders, and many tools to store the paperwork and organise it so that they could easily retrieve, through some documented process, at a later stage whatever they needed. That’s where the name filesystem in the computer world emerges from and this is oneofthe subject of this episode. We’re going to discuss data storage on Unix with some discussion about filesys- tem and an emphasis on storage device. -
Arxiv:1901.01161V1 [Cs.CR] 4 Jan 2019
Page Cache Attacks Daniel Gruss1, Erik Kraft1, Trishita Tiwari2, Michael Schwarz1, Ari Trachtenberg2, Jason Hennessey3, Alex Ionescu4, Anders Fogh5 1 Graz University of Technology, 2 Boston University, 3 NetApp, 4 CrowdStrike, 5 Intel Corporation Abstract last twenty years [3, 40, 53]. Osvik et al. [51] showed that an attacker can observe the cache state at the granularity of We present a new hardware-agnostic side-channel attack that a cache set using Prime+Probe, and later Yarom et al. [77] targets one of the most fundamental software caches in mod- showed this with cache line granularity using Flush+Reload. ern computer systems: the operating system page cache. The While different cache attacks have different use cases, the page cache is a pure software cache that contains all disk- accuracy of Flush+Reload remains unrivaled. backed pages, including program binaries, shared libraries, Indeed, virtually all Flush+Reload attacks target pages in and other files, and our attacks thus work across cores and the so-called page cache [30, 33, 34, 35, 42, 77]. The page CPUs. Our side-channel permits unprivileged monitoring of cache is a pure software cache implemented in all major op- some memory accesses of other processes, with a spatial res- erating systems today, and it contains virtually all pages in olution of 4 kB and a temporal resolution of 2 µs on Linux use. Pages that contain data accessible to multiple programs, (restricted to 6:7 measurements per second) and 466 ns on such as disk-backed pages (e.g., program binaries, shared li- Windows (restricted to 223 measurements per second); this braries, other files, etc.), are shared among all processes re- is roughly the same order of magnitude as the current state- gardless of privilege and permission boundaries [24]. -
An Updated Overview of the QEMU Storage Stack
An Updated Overview of the QEMU Storage Stack Stefan Hajnoczi ± [email protected] Open Virtualization IBM Linux Technology Center 2011 Linux is a registered trademark of Linus Torvalds. The topic ● What is the QEMU storage stack? ● Configuring the storage stack ● Recent and future developments – “Cautionary statement regarding forward- looking statements” QEMU and its uses ● “QEMU is a generic and open source machine emulator and virtualizer” – http://www.qemu.org/ ● Emulation: – For cross-compilation, development environments – Android Emulator, shipping in an Android SDK near you ● Virtualization: – KVM and Xen use QEMU device emulation Storage in QEMU ● Devices and media: – Floppy, CD-ROM, USB stick, SD card, harddisk ● Host storage: – Flat files (img, iso) ● Also over NFS – CD-ROM host device (/dev/cdrom) – Block devices (/dev/sda3, LVM volumes, iSCSI LUNs) – Distributed storage (Sheepdog, Ceph) QEMU -drive option qemu -drive if=ide|virtio|scsi, file=path/to/img, cache=writethrough|writeback|none|unsafe ● Storage interface is set with if= ● Path to image file or device is set with path= ● Caching mode is set with cache= ● More on what this means later, but first the picture of the overall storage stack... The QEMU storage stack Application ·Application and guest kernel work similar to bare metal. File system & block layer ·Guest talks to QEMU via Driver emulated hardware. Hardware emulation ·QEMU performs I/O to an image file on behalf of the Image format (optional) guest. File system & block layer ·Host kernel treats guest I/O like any userspace Driver application. Guest QEMU Host Seeing double ● There may be two file systems. -
Using Test Beds to Probe Non- Volatile Memories' Dark
Beyond the Datasheet: Using Test Beds to Probe Non- Volatile Memories’ Dark Secrets Laura M. Grupp*, Adrian M. Caulfield*, Joel Coburn*, John D. Davis†, and Steven Swanson* *University of California, San Diego †Microsoft Research, Silicon Valley Lab [lgrupp, acaulfie, jdcoburn, swanson]@cs.ucsd.edu [email protected] latency, bandwidth, bit error rate, and energy efficiency will all combine to determine which designs are viable Abstract and which are not. Second, they must understand how Non-volatile memories (such as NAND flash and phase the non-volatile memory array will affect the rest of the change memories) have the potential to revolutionize system. If a new storage technology significantly computer systems. However, these technologies have increases performance, it may expose bottlenecks in complex behavior in terms of performance, reliability, other parts of the system. and energy consumption that make fully exploiting Addressing the first challenge requires a their potential a complicated task. As device engineers comprehensive understanding of non-volatile memory push bit densities higher, this complexity will only performance, failure modes, and energy consumption. increase. Managing and exploiting the complex and at This information is difficult to come by. Device times surprising behavior of these memories requires a manufacturers hide the details of their devices behind deep understanding of the devices grounded in non-disclosure agreements, and even when datasheets experimental results. Our research groups have are freely available, they provide scant details: average developed several hardware test beds for flash and performance characteristics, vague recommendations other memories that allow us to both characterize these about error correction requirements, and inexact memories and experimentally evaluate their bounds on energy consumption.