Enterprise Filesystems
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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. -
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 -
Trusted Docker Containers and Trusted Vms in Openstack
Trusted Docker Containers and Trusted VMs in OpenStack Raghu Yeluri Abhishek Gupta Outline o Context: Docker Security – Top Customer Asks o Intel’s Focus: Trusted Docker Containers o Who Verifies Trust ? o Reference Architecture with OpenStack o Demo o Availability o Call to Action Docker Overview in a Slide.. Docker Hub Lightweight, open source engine for creating, deploying containers Provides work flow for running, building and containerizing apps. Separates apps from where they run.; Enables Micro-services; scale by composition. Underlying building blocks: Linux kernel's namespaces (isolation) + cgroups (resource control) + .. Components of Docker Docker Engine – Runtime for running, building Docker containers. Docker Repositories(Hub) - SaaS service for sharing/managing images Docker Images (layers) Images hold Apps. Shareable snapshot of software. Container is a running instance of image. Orchestration: OpenStack, Docker Swarm, Kubernetes, Mesos, Fleet, Project Docker Layers Atomic, Lattice… Docker Security – 5 key Customer Asks 1. How do you know that the Docker Host Integrity is there? o Do you trust the Docker daemon? o Do you trust the Docker host has booted with Integrity? 2. How do you verify Docker Container Integrity o Who wrote the Docker image? Do you trust the image? Did the right Image get launched? 3. Runtime Protection of Docker Engine & Enhanced Isolation o How can Intel help with runtime Integrity? 4. Enterprise Security Features – Compliance, Manageability, Identity authentication.. Etc. 5. OpenStack as a single Control Plane for Trusted VMs and Trusted Docker Containers.. Intel’s Focus: Enable Hardware-based Integrity Assurance for Docker Containers – Trusted Docker Containers Trusted Docker Containers – 3 focus areas o Launch Integrity of Docker Host o Runtime Integrity of Docker Host o Integrity of Docker Images Today’s Focus: Integrity of Docker Host, and how to use it in OpenStack. -
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. -
Study of File System Evolution
Study of File System Evolution Swaminathan Sundararaman, Sriram Subramanian Department of Computer Science University of Wisconsin {swami, srirams} @cs.wisc.edu Abstract File systems have traditionally been a major area of file systems are typically developed and maintained by research and development. This is evident from the several programmer across the globe. At any point in existence of over 50 file systems of varying popularity time, for a file system, there are three to six active in the current version of the Linux kernel. They developers, ten to fifteen patch contributors but a single represent a complex subsystem of the kernel, with each maintainer. These people communicate through file system employing different strategies for tackling individual file system mailing lists [14, 16, 18] various issues. Although there are many file systems in submitting proposals for new features, enhancements, Linux, there has been no prior work (to the best of our reporting bugs, submitting and reviewing patches for knowledge) on understanding how file systems evolve. known bugs. The problems with the open source We believe that such information would be useful to the development approach is that all communication is file system community allowing developers to learn buried in the mailing list archives and aren’t easily from previous experiences. accessible to others. As a result when new file systems are developed they do not leverage past experience and This paper looks at six file systems (Ext2, Ext3, Ext4, could end up re-inventing the wheel. To make things JFS, ReiserFS, and XFS) from a historical perspective worse, people could typically end up doing the same (between kernel versions 1.0 to 2.6) to get an insight on mistakes as done in other file systems. -
Serverless Network File Systems
Serverless Network File Systems Thomas E. Anderson, Michael D. Dahlin, Jeanna M. Neefe, David A. Patterson, Drew S. Roselli, and Randolph Y. Wang Computer Science Division University of California at Berkeley Abstract In this paper, we propose a new paradigm for network file system design, serverless network file systems. While traditional network file systems rely on a central server machine, a serverless system utilizes workstations cooperating as peers to provide all file system services. Any machine in the system can store, cache, or control any block of data. Our approach uses this location independence, in combination with fast local area networks, to provide better performance and scalability than traditional file systems. Further, because any machine in the system can assume the responsibilities of a failed component, our serverless design also provides high availability via redundant data storage. To demonstrate our approach, we have implemented a prototype serverless network file system called xFS. Preliminary performance measurements suggest that our architecture achieves its goal of scalability. For instance, in a 32-node xFS system with 32 active clients, each client receives nearly as much read or write throughput as it would see if it were the only active client. 1. Introduction A serverless network file system distributes storage, cache, and control over cooperating workstations. This approach contrasts with traditional file systems such as Netware [Majo94], NFS [Sand85], Andrew [Howa88], and Sprite [Nels88] where a central server machine stores all data and satisfies all client cache misses. Such a central server is both a performance and reliability bottleneck. A serverless system, on the other hand, distributes control processing and data storage to achieve scalable high performance, migrates the responsibilities of failed components to the remaining machines to provide high availability, and scales gracefully to simplify system management. -
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. -
Providing User Security Guarantees in Public Infrastructure Clouds
1 Providing User Security Guarantees in Public Infrastructure Clouds Nicolae Paladi, Christian Gehrmann, and Antonis Michalas Abstract—The infrastructure cloud (IaaS) service model offers improved resource flexibility and availability, where tenants – insulated from the minutiae of hardware maintenance – rent computing resources to deploy and operate complex systems. Large-scale services running on IaaS platforms demonstrate the viability of this model; nevertheless, many organizations operating on sensitive data avoid migrating operations to IaaS platforms due to security concerns. In this paper, we describe a framework for data and operation security in IaaS, consisting of protocols for a trusted launch of virtual machines and domain-based storage protection. We continue with an extensive theoretical analysis with proofs about protocol resistance against attacks in the defined threat model. The protocols allow trust to be established by remotely attesting host platform configuration prior to launching guest virtual machines and ensure confidentiality of data in remote storage, with encryption keys maintained outside of the IaaS domain. Presented experimental results demonstrate the validity and efficiency of the proposed protocols. The framework prototype was implemented on a test bed operating a public electronic health record system, showing that the proposed protocols can be integrated into existing cloud environments. Index Terms—Security; Cloud Computing; Storage Protection; Trusted Computing F 1 INTRODUCTION host level. While support data encryption at rest is offered by several cloud providers and can be configured by tenants Cloud computing has progressed from a bold vision to mas- in their VM instances, functionality and migration capabil- sive deployments in various application domains. However, ities of such solutions are severely restricted. -
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. -
File Handling in Python
hapter C File Handling in 2 Python There are many ways of trying to understand programs. People often rely too much on one way, which is called "debugging" and consists of running a partly- understood program to see if it does what you expected. Another way, which ML advocates, is to install some means of understanding in the very programs themselves. — Robin Milner In this Chapter » Introduction to Files » Types of Files » Opening and Closing a 2.1 INTRODUCTION TO FILES Text File We have so far created programs in Python that » Writing to a Text File accept the input, manipulate it and display the » Reading from a Text File output. But that output is available only during » Setting Offsets in a File execution of the program and input is to be entered through the keyboard. This is because the » Creating and Traversing a variables used in a program have a lifetime that Text File lasts till the time the program is under execution. » The Pickle Module What if we want to store the data that were input as well as the generated output permanently so that we can reuse it later? Usually, organisations would want to permanently store information about employees, inventory, sales, etc. to avoid repetitive tasks of entering the same data. Hence, data are stored permanently on secondary storage devices for reusability. We store Python programs written in script mode with a .py extension. Each program is stored on the secondary device as a file. Likewise, the data entered, and the output can be stored permanently into a file. -
In Search of the Ideal Storage Configuration for Docker Containers
In Search of the Ideal Storage Configuration for Docker Containers Vasily Tarasov1, Lukas Rupprecht1, Dimitris Skourtis1, Amit Warke1, Dean Hildebrand1 Mohamed Mohamed1, Nagapramod Mandagere1, Wenji Li2, Raju Rangaswami3, Ming Zhao2 1IBM Research—Almaden 2Arizona State University 3Florida International University Abstract—Containers are a widely successful technology today every running container. This would cause a great burden on popularized by Docker. Containers improve system utilization by the I/O subsystem and make container start time unacceptably increasing workload density. Docker containers enable seamless high for many workloads. As a result, copy-on-write (CoW) deployment of workloads across development, test, and produc- tion environments. Docker’s unique approach to data manage- storage and storage snapshots are popularly used and images ment, which involves frequent snapshot creation and removal, are structured in layers. A layer consists of a set of files and presents a new set of exciting challenges for storage systems. At layers with the same content can be shared across images, the same time, storage management for Docker containers has reducing the amount of storage required to run containers. remained largely unexplored with a dizzying array of solution With Docker, one can choose Aufs [6], Overlay2 [7], choices and configuration options. In this paper we unravel the multi-faceted nature of Docker storage and demonstrate its Btrfs [8], or device-mapper (dm) [9] as storage drivers which impact on system and workload performance. As we uncover provide the required snapshotting and CoW capabilities for new properties of the popular Docker storage drivers, this is a images. None of these solutions, however, were designed with sobering reminder that widespread use of new technologies can Docker in mind and their effectiveness for Docker has not been often precede their careful evaluation.