Filesystem Considerations for Embedded Devices ELC2015 03/25/15
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The Kernel Report
The kernel report (ELC 2012 edition) Jonathan Corbet LWN.net [email protected] The Plan Look at a year's worth of kernel work ...with an eye toward the future Starting off 2011 2.6.37 released - January 4, 2011 11,446 changes, 1,276 developers VFS scalability work (inode_lock removal) Block I/O bandwidth controller PPTP support Basic pNFS support Wakeup sources What have we done since then? Since 2.6.37: Five kernel releases have been made 59,000 changes have been merged 3069 developers have contributed to the kernel 416 companies have supported kernel development February As you can see in these posts, Ralink is sending patches for the upstream rt2x00 driver for their new chipsets, and not just dumping a huge, stand-alone tarball driver on the community, as they have done in the past. This shows a huge willingness to learn how to deal with the kernel community, and they should be strongly encouraged and praised for this major change in attitude. – Greg Kroah-Hartman, February 9 Employer contributions 2.6.38-3.2 Volunteers 13.9% Wolfson Micro 1.7% Red Hat 10.9% Samsung 1.6% Intel 7.3% Google 1.6% unknown 6.9% Oracle 1.5% Novell 4.0% Microsoft 1.4% IBM 3.6% AMD 1.3% TI 3.4% Freescale 1.3% Broadcom 3.1% Fujitsu 1.1% consultants 2.2% Atheros 1.1% Nokia 1.8% Wind River 1.0% Also in February Red Hat stops releasing individual kernel patches March 2.6.38 released – March 14, 2011 (9,577 changes from 1198 developers) Per-session group scheduling dcache scalability patch set Transmit packet steering Transparent huge pages Hierarchical block I/O bandwidth controller Somebody needs to get a grip in the ARM community. -
MLNX OFED Documentation Rev 5.0-2.1.8.0
MLNX_OFED Documentation Rev 5.0-2.1.8.0 Exported on May/21/2020 06:13 AM https://docs.mellanox.com/x/JLV-AQ Notice This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. This document is not a commitment to develop, release, or deliver any Material (defined below), code, or functionality. NVIDIA reserves the right to make corrections, modifications, enhancements, improvements, and any other changes to this document, at any time without notice. Customer should obtain the latest relevant information before placing orders and should verify that such information is current and complete. NVIDIA products are sold subject to the NVIDIA standard terms and conditions of sale supplied at the time of order acknowledgement, unless otherwise agreed in an individual sales agreement signed by authorized representatives of NVIDIA and customer (“Terms of Sale”). NVIDIA hereby expressly objects to applying any customer general terms and conditions with regards to the purchase of the NVIDIA product referenced in this document. No contractual obligations are formed either directly or indirectly by this document. NVIDIA products are not designed, authorized, or warranted to be suitable for use in medical, military, aircraft, space, or life support equipment, nor in applications where failure or malfunction of the NVIDIA product can reasonably be expected to result in personal injury, death, or property or environmental damage. -
Storage Administration Guide Storage Administration Guide SUSE Linux Enterprise Server 12 SP4
SUSE Linux Enterprise Server 12 SP4 Storage Administration Guide Storage Administration Guide SUSE Linux Enterprise Server 12 SP4 Provides information about how to manage storage devices on a SUSE Linux Enterprise Server. Publication Date: September 24, 2021 SUSE LLC 1800 South Novell Place Provo, UT 84606 USA https://documentation.suse.com Copyright © 2006– 2021 SUSE LLC and contributors. All rights reserved. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or (at your option) version 1.3; with the Invariant Section being this copyright notice and license. A copy of the license version 1.2 is included in the section entitled “GNU Free Documentation License”. For SUSE trademarks, see https://www.suse.com/company/legal/ . All other third-party trademarks are the property of their respective owners. Trademark symbols (®, ™ etc.) denote trademarks of SUSE and its aliates. Asterisks (*) denote third-party trademarks. All information found in this book has been compiled with utmost attention to detail. However, this does not guarantee complete accuracy. Neither SUSE LLC, its aliates, the authors nor the translators shall be held liable for possible errors or the consequences thereof. Contents About This Guide xii 1 Available Documentation xii 2 Giving Feedback xiv 3 Documentation Conventions xiv 4 Product Life Cycle and Support xvi Support Statement for SUSE Linux Enterprise Server xvii • Technology Previews xviii I FILE SYSTEMS AND MOUNTING 1 1 Overview -
Rootless Containers with Podman and Fuse-Overlayfs
CernVM Workshop 2019 (4th June 2019) Rootless containers with Podman and fuse-overlayfs Giuseppe Scrivano @gscrivano Introduction 2 Rootless Containers • “Rootless containers refers to the ability for an unprivileged user (i.e. non-root user) to create, run and otherwise manage containers.” (https://rootlesscontaine.rs/ ) • Not just about running the container payload as an unprivileged user • Container runtime runs also as an unprivileged user 3 Don’t confuse with... • sudo podman run --user foo – Executes the process in the container as non-root – Podman and the OCI runtime still running as root • USER instruction in Dockerfile – same as above – Notably you can’t RUN dnf install ... 4 Don’t confuse with... • podman run --uidmap – Execute containers as a non-root user, using user namespaces – Most similar to rootless containers, but still requires podman and runc to run as root 5 Motivation of Rootless Containers • To mitigate potential vulnerability of container runtimes • To allow users of shared machines (e.g. HPC) to run containers without the risk of breaking other users environments • To isolate nested containers 6 Caveat: Not a panacea • Although rootless containers could mitigate these vulnerabilities, it is not a panacea , especially it is powerless against kernel (and hardware) vulnerabilities – CVE 2013-1858, CVE-2015-1328, CVE-2018-18955 • Castle approach : it should be used in conjunction with other security layers such as seccomp and SELinux 7 Podman 8 Rootless Podman Podman is a daemon-less alternative to Docker • $ alias -
System Calls System Calls
System calls We will investigate several issues related to system calls. Read chapter 12 of the book Linux system call categories file management process management error handling note that these categories are loosely defined and much is behind included, e.g. communication. Why? 1 System calls File management system call hierarchy you may not see some topics as part of “file management”, e.g., sockets 2 System calls Process management system call hierarchy 3 System calls Error handling hierarchy 4 Error Handling Anything can fail! System calls are no exception Try to read a file that does not exist! Error number: errno every process contains a global variable errno errno is set to 0 when process is created when error occurs errno is set to a specific code associated with the error cause trying to open file that does not exist sets errno to 2 5 Error Handling error constants are defined in errno.h here are the first few of errno.h on OS X 10.6.4 #define EPERM 1 /* Operation not permitted */ #define ENOENT 2 /* No such file or directory */ #define ESRCH 3 /* No such process */ #define EINTR 4 /* Interrupted system call */ #define EIO 5 /* Input/output error */ #define ENXIO 6 /* Device not configured */ #define E2BIG 7 /* Argument list too long */ #define ENOEXEC 8 /* Exec format error */ #define EBADF 9 /* Bad file descriptor */ #define ECHILD 10 /* No child processes */ #define EDEADLK 11 /* Resource deadlock avoided */ 6 Error Handling common mistake for displaying errno from Linux errno man page: 7 Error Handling Description of the perror () system call. -
A Brief History of UNIX File Systems
A Brief History of UNIX File Systems Val Henson IBM, Inc. [email protected] Summary • Review of UNIX file system concepts • File system formats, 1974-2004 • File system comparisons and recommendations • Fun trivia • Questions and answers (corrections ONLY during talk) 1 VFS/vnode architecture • VFS: Virtual File System: common object-oriented interface to fs's • vnode: virtual node: abstract file object, includes vnode ops • All operations to fs's and files done through VFS/vnode in- terface • S.R. Kleiman, \Vnodes: An Architecture for Multiple File System Types in Sun UNIX," Summer USENIX 1986 2 Some Definitions superblock: fs summary, pointers to other information inode: on-disk structure containing information about a file indirect block: block containing pointers to other blocks metadata: everything that is not user data, including directory entries 3 Disk characteristics • Track - contiguous region, can be read at maximum speed • Seek time - time to move the head between different tracks • Rotational delay - time for part of track to move under head • Fixed per I/O overhead means bigger I/Os are better 4 In the beginning: System V FS (S5FS) (c. 1974) • First UNIX file system, referred to as \FS" • Disk layout: superblock, inodes, followed by everything else • 512-1024 byte block size, no fragments • Super simple - and super slow! 2-5% of raw disk bandwidth 5 Berkeley Fast File System (FFS or UFS) (c. 1984) • Metadata spread throughout the disk in \cylinder groups" • Block size 4KB minimum, frag size 1KB (to avoid 45% wasted space) • Physical -
F2FS) Overview
Flash Friendly File System (F2FS) Overview Leon Romanovsky [email protected] www.leon.nu November 17, 2012 Leon Romanovsky [email protected] F2FS Overview Disclaimer Everything in this lecture shall not, under any circumstances, hold any legal liability whatsoever. Any usage of the data and information in this document shall be solely on the responsibility of the user. This lecture is not given on behalf of any company or organization. Leon Romanovsky [email protected] F2FS Overview Introduction: Flash Memory Definition Flash memory is a non-volatile storage device that can be electrically erased and reprogrammed. Challenges block-level access wear leveling read disturb bad blocks management garbage collection different physics different interfaces Leon Romanovsky [email protected] F2FS Overview Introduction: Flash Memory Definition Flash memory is a non-volatile storage device that can be electrically erased and reprogrammed. Challenges block-level access wear leveling read disturb bad blocks management garbage collection different physics different interfaces Leon Romanovsky [email protected] F2FS Overview Introduction: General System Architecture Leon Romanovsky [email protected] F2FS Overview Introduction: File Systems Optimized for disk storage EXT2/3/4 BTRFS VFAT Optimized for flash, but not aware of FTL JFFS/JFFS2 YAFFS LogFS UbiFS NILFS Leon Romanovsky [email protected] F2FS Overview Background: LFS vs. Unix FS Leon Romanovsky [email protected] F2FS Overview Background: LFS Overview Leon Romanovsky [email protected] F2FS Overview Background: LFS Garbage Collection 1 A victim segment is selected through referencing segment usage table. 2 It loads parent index structures of all the data in the victim identified by segment summary blocks. -
Dm-X: Protecting Volume-Level Integrity for Cloud Volumes and Local
dm-x: Protecting Volume-level Integrity for Cloud Volumes and Local Block Devices Anrin Chakraborti Bhushan Jain Jan Kasiak Stony Brook University Stony Brook University & Stony Brook University UNC, Chapel Hill Tao Zhang Donald Porter Radu Sion Stony Brook University & Stony Brook University & Stony Brook University UNC, Chapel Hill UNC, Chapel Hill ABSTRACT on Amazon’s Virtual Private Cloud (VPC) [2] (which provides The verified boot feature in recent Android devices, which strong security guarantees through network isolation) store deploys dm-verity, has been overwhelmingly successful in elim- data on untrusted storage devices residing in the public cloud. inating the extremely popular Android smart phone rooting For example, Amazon VPC file systems, object stores, and movement [25]. Unfortunately, dm-verity integrity guarantees databases reside on virtual block devices in the Amazon are read-only and do not extend to writable volumes. Elastic Block Storage (EBS). This paper introduces a new device mapper, dm-x, that This allows numerous attack vectors for a malicious cloud efficiently (fast) and reliably (metadata journaling) assures provider/untrusted software running on the server. For in- volume-level integrity for entire, writable volumes. In a direct stance, to ensure SEC-mandated assurances of end-to-end disk setup, dm-x overheads are around 6-35% over ext4 on the integrity, banks need to guarantee that the external cloud raw volume while offering additional integrity guarantees. For storage service is unable to remove log entries documenting cloud storage (Amazon EBS), dm-x overheads are negligible. financial transactions. Yet, without integrity of the storage device, a malicious cloud storage service could remove logs of a coordinated attack. -
Enhancing the Accuracy of Synthetic File System Benchmarks Salam Farhat Nova Southeastern University, [email protected]
Nova Southeastern University NSUWorks CEC Theses and Dissertations College of Engineering and Computing 2017 Enhancing the Accuracy of Synthetic File System Benchmarks Salam Farhat Nova Southeastern University, [email protected] This document is a product of extensive research conducted at the Nova Southeastern University College of Engineering and Computing. For more information on research and degree programs at the NSU College of Engineering and Computing, please click here. Follow this and additional works at: https://nsuworks.nova.edu/gscis_etd Part of the Computer Sciences Commons Share Feedback About This Item NSUWorks Citation Salam Farhat. 2017. Enhancing the Accuracy of Synthetic File System Benchmarks. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (1003) https://nsuworks.nova.edu/gscis_etd/1003. This Dissertation is brought to you by the College of Engineering and Computing at NSUWorks. It has been accepted for inclusion in CEC Theses and Dissertations by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Enhancing the Accuracy of Synthetic File System Benchmarks by Salam Farhat A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor in Philosophy in Computer Science College of Engineering and Computing Nova Southeastern University 2017 We hereby certify that this dissertation, submitted by Salam Farhat, conforms to acceptable standards and is fully adequate in scope and quality to fulfill the dissertation requirements for the degree of Doctor of Philosophy. _____________________________________________ ________________ Gregory E. Simco, Ph.D. Date Chairperson of Dissertation Committee _____________________________________________ ________________ Sumitra Mukherjee, Ph.D. Date Dissertation Committee Member _____________________________________________ ________________ Francisco J. -
In Search of Optimal Data Placement for Eliminating Write Amplification in Log-Structured Storage
In Search of Optimal Data Placement for Eliminating Write Amplification in Log-Structured Storage Qiuping Wangy, Jinhong Liy, Patrick P. C. Leey, Guangliang Zhao∗, Chao Shi∗, Lilong Huang∗ yThe Chinese University of Hong Kong ∗Alibaba Group ABSTRACT invalidated by a live block; a.k.a. the death time [12]) to achieve Log-structured storage has been widely deployed in various do- the minimum possible WA. However, without obtaining the fu- mains of storage systems for high performance. However, its garbage ture knowledge of the BIT pattern, how to design an optimal data collection (GC) incurs severe write amplification (WA) due to the placement scheme with the minimum WA remains an unexplored frequent rewrites of live data. This motivates many research stud- issue. Existing temperature-based data placement schemes that ies, particularly on data placement strategies, that mitigate WA in group blocks by block temperatures (e.g., write/update frequencies) log-structured storage. We show how to design an optimal data [7, 16, 22, 27, 29, 35, 36] are arguably inaccurate to capture the BIT placement scheme that leads to the minimum WA with the fu- pattern and fail to group the blocks with similar BITs [12]. ture knowledge of block invalidation time (BIT) of each written To this end, we propose SepBIT, a novel data placement scheme block. Guided by this observation, we propose SepBIT, a novel data that aims to minimize the WA in log-structured storage. It infers placement algorithm that aims to minimize WA in log-structured the BITs of written blocks from the underlying storage workloads storage. -
Fragmentation in Large Object Repositories
Fragmentation in Large Object Repositories Experience Paper Russell Sears Catharine van Ingen University of California, Berkeley Microsoft Research [email protected] [email protected] ABSTRACT 1. INTRODUCTION Fragmentation leads to unpredictable and degraded application Application data objects continue to increase in size. performance. While these problems have been studied in detail Furthermore, the increasing popularity of web services and other for desktop filesystem workloads, this study examines newer network applications means that systems that once managed static systems such as scalable object stores and multimedia archives of “finished” objects now manage frequently modified repositories. Such systems use a get/put interface to store objects. versions of application data. Rather than updating these objects in In principle, databases and filesystems can support such place, typical archives either store multiple versions of the objects applications efficiently, allowing system designers to focus on (the V of WebDAV stands for “versioning” [25]), or simply do complexity, deployment cost and manageability. wholesale replacement (as in SharePoint Team Services [19]). Similarly, applications such as personal video recorders and Although theoretical work proves that certain storage policies media subscription servers continuously allocate and delete large, behave optimally for some workloads, these policies often behave transient objects. poorly in practice. Most storage benchmarks focus on short-term behavior or do not measure fragmentation. We compare SQL Applications store large objects as some combination of files in Server to NTFS and find that fragmentation dominates the filesystem and as BLOBs (binary large objects) in a database. performance when object sizes exceed 256KB-1MB. NTFS Only folklore is available regarding the tradeoffs. -
ECE 598 – Advanced Operating Systems Lecture 19
ECE 598 { Advanced Operating Systems Lecture 19 Vince Weaver http://web.eece.maine.edu/~vweaver [email protected] 7 April 2016 Announcements • Homework #7 was due • Homework #8 will be posted 1 Why use FAT over ext2? • FAT simpler, easy to code • FAT supported on all major OSes • ext2 faster, more robust filename and permissions 2 btrfs • B-tree fs (similar to a binary tree, but with pages full of leaves) • overwrite filesystem (overwite on modify) vs CoW • Copy on write. When write to a file, old data not overwritten. Since old data not over-written, crash recovery better Eventually old data garbage collected • Data in extents 3 • Copy-on-write • Forest of trees: { sub-volumes { extent-allocation { checksum tree { chunk device { reloc • On-line defragmentation • On-line volume growth 4 • Built-in RAID • Transparent compression • Snapshots • Checksums on data and meta-data • De-duplication • Cloning { can make an exact snapshot of file, copy-on- write different than link, different inodles but same blocks 5 Embedded • Designed to be small, simple, read-only? • romfs { 32 byte header (magic, size, checksum,name) { Repeating files (pointer to next [0 if none]), info, size, checksum, file name, file data • cramfs 6 ZFS Advanced OS from Sun/Oracle. Similar in idea to btrfs indirect still, not extent based? 7 ReFS Resilient FS, Microsoft's answer to brtfs and zfs 8 Networked File Systems • Allow a centralized file server to export a filesystem to multiple clients. • Provide file level access, not just raw blocks (NBD) • Clustered filesystems also exist, where multiple servers work in conjunction.