An Introduction to Cgroups and Cgroupspy Tags = [‘Python’, ’Docker’, ‘Coreos', ‘Systemd']
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Security Assurance Requirements for Linux Application Container Deployments
NISTIR 8176 Security Assurance Requirements for Linux Application Container Deployments Ramaswamy Chandramouli This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8176 NISTIR 8176 Security Assurance Requirements for Linux Application Container Deployments Ramaswamy Chandramouli Computer Security Division Information Technology Laboratory This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8176 October 2017 U.S. Department of Commerce Wilbur L. Ross, Jr., Secretary National Institute of Standards and Technology Walter Copan, NIST Director and Under Secretary of Commerce for Standards and Technology NISTIR 8176 SECURITY ASSURANCE FOR LINUX CONTAINERS National Institute of Standards and Technology Internal Report 8176 37 pages (October 2017) This publication is available free of charge from: https://doi.org/10.6028/NIST.IR.8176 Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. This p There may be references in this publication to other publications currently under development by NIST in accordance with its assigned statutory responsibilities. The information in this publication, including concepts and methodologies, may be used by federal agencies even before the completion of such companion publications. Thus, until each ublication is available free of charge from: http publication is completed, current requirements, guidelines, and procedures, where they exist, remain operative. For planning and transition purposes, federal agencies may wish to closely follow the development of these new publications by NIST. -
Hypervisors Vs. Lightweight Virtualization: a Performance Comparison
2015 IEEE International Conference on Cloud Engineering Hypervisors vs. Lightweight Virtualization: a Performance Comparison Roberto Morabito, Jimmy Kjällman, and Miika Komu Ericsson Research, NomadicLab Jorvas, Finland [email protected], [email protected], [email protected] Abstract — Virtualization of operating systems provides a container and alternative solutions. The idea is to quantify the common way to run different services in the cloud. Recently, the level of overhead introduced by these platforms and the lightweight virtualization technologies claim to offer superior existing gap compared to a non-virtualized environment. performance. In this paper, we present a detailed performance The remainder of this paper is structured as follows: in comparison of traditional hypervisor based virtualization and Section II, literature review and a brief description of all the new lightweight solutions. In our measurements, we use several technologies and platforms evaluated is provided. The benchmarks tools in order to understand the strengths, methodology used to realize our performance comparison is weaknesses, and anomalies introduced by these different platforms in terms of processing, storage, memory and network. introduced in Section III. The benchmark results are presented Our results show that containers achieve generally better in Section IV. Finally, some concluding remarks and future performance when compared with traditional virtual machines work are provided in Section V. and other recent solutions. Albeit containers offer clearly more dense deployment of virtual machines, the performance II. BACKGROUND AND RELATED WORK difference with other technologies is in many cases relatively small. In this section, we provide an overview of the different technologies included in the performance comparison. -
Multicore Operating-System Support for Mixed Criticality∗
Multicore Operating-System Support for Mixed Criticality∗ James H. Anderson, Sanjoy K. Baruah, and Bjorn¨ B. Brandenburg The University of North Carolina at Chapel Hill Abstract Different criticality levels provide different levels of assurance against failure. In current avionics designs, Ongoing research is discussed on the development of highly-critical software is kept physically separate from operating-system support for enabling mixed-criticality less-critical software. Moreover, no currently deployed air- workloads to be supported on multicore platforms. This craft uses multicore processors to host highly-critical tasks work is motivated by avionics systems in which such work- (more precisely, if multicore processors are used, and if loads occur. In the mixed-criticality workload model that is such a processor hosts highly-critical applications, then all considered, task execution costs may be determined using but one of its cores are turned off). These design decisions more-stringent methods at high criticality levels, and less- are largely driven by certification issues. For example, cer- stringent methods at low criticality levels. The main focus tifying highly-critical components becomes easier if poten- of this research effort is devising mechanisms for providing tially adverse interactions among components executing on “temporal isolation” across criticality levels: lower levels different cores through shared hardware such as caches are should not adversely “interfere” with higher levels. simply “defined” not to occur. Unfortunately, hosting an overall workload as described here clearly wastes process- ing resources. 1 Introduction In this paper, we propose operating-system infrastruc- ture that allows applications of different criticalities to be The evolution of computational frameworks for avionics co-hosted on a multicore platform. -
Systemd As a Container Manager
systemd as a Container Manager Seth Jennings [email protected] Texas Linux Fest 2015 8/21/2015 Agenda ● Very quick overview of systemd ● What is a Linux Container ● systemd as a Container Manager ● Live Demo! Because I like to punish myself! Disclaimer What is systemd? ● systemd is a suite of system management daemons, libraries, and utilities designed as a central management and configuration platform for the Linux operating system. How Big Is This “Suite” ● systemd - init process, pid 1 ● journald ● logind ● udevd ● hostnamed ● machined ● importd ● networkd ● resolved ● localed ● timedated ● timesyncd ● and more! Don't Leave! ● No deep dive on all of these ● Focus on using systemd for container management – Spoiler alert: many of the systemd commands you already use work on containers managed by systemd too! What is a Linux Container ● What it is not – Magic ● conjured only from the mystical language of Go – Virtualization (hardware emulation) – A completely new concept never before conceived of by man since time began – An image format – An image distribution mechanism – Only usable by modular (microservice) applications at scale What is a Linux Container ● A resource-constrained, namespaced environment, initialized by a container manager and enforced by the kernel, where processes can run – kernel cgroups limits hardware resources ● cpus, memory, i/o ● special cgroup filesystem /sys/fs/cgroup – kernel namespacing limits resource visibility ● mount, PID, user, network, UTS, IPC ● syscalls clone(), setns(), unshare() What is a Linux Container ● The set of processes in the container is rooted in a process that has pid 1 inside the pid namespace of the container ● The filesystem inside the container can be as complex as a docker image or as simple as a subdirectory on the host (think chroot). -
Resource Management: Linux Kernel Namespaces and Cgroups
Resource management: Linux kernel Namespaces and cgroups Rami Rosen [email protected] Haifux, May 2013 www.haifux.org 1/121 http://ramirose.wix.com/ramirosen TOC Network Namespace PID namespaces UTS namespace Mount namespace user namespaces cgroups Mounting cgroups links Note: All code examples are from for_3_10 branch of cgroup git tree (3.9.0-rc1, April 2013) 2/121 http://ramirose.wix.com/ramirosen General The presentation deals with two Linux process resource management solutions: namespaces and cgroups. We will look at: ● Kernel Implementation details. ●what was added/changed in brief. ● User space interface. ● Some working examples. ● Usage of namespaces and cgroups in other projects. ● Is process virtualization indeed lightweight comparing to Os virtualization ? ●Comparing to VMWare/qemu/scaleMP or even to Xen/KVM. 3/121 http://ramirose.wix.com/ramirosen Namespaces ● Namespaces - lightweight process virtualization. – Isolation: Enable a process (or several processes) to have different views of the system than other processes. – 1992: “The Use of Name Spaces in Plan 9” – http://www.cs.bell-labs.com/sys/doc/names.html ● Rob Pike et al, ACM SIGOPS European Workshop 1992. – Much like Zones in Solaris. – No hypervisor layer (as in OS virtualization like KVM, Xen) – Only one system call was added (setns()) – Used in Checkpoint/Restart ● Developers: Eric W. biederman, Pavel Emelyanov, Al Viro, Cyrill Gorcunov, more. – 4/121 http://ramirose.wix.com/ramirosen Namespaces - contd There are currently 6 namespaces: ● mnt (mount points, filesystems) ● pid (processes) ● net (network stack) ● ipc (System V IPC) ● uts (hostname) ● user (UIDs) 5/121 http://ramirose.wix.com/ramirosen Namespaces - contd It was intended that there will be 10 namespaces: the following 4 namespaces are not implemented (yet): ● security namespace ● security keys namespace ● device namespace ● time namespace. -
Container-Based Virtualization for Byte-Addressable NVM Data Storage
2016 IEEE International Conference on Big Data (Big Data) Container-Based Virtualization for Byte-Addressable NVM Data Storage Ellis R. Giles Rice University Houston, Texas [email protected] Abstract—Container based virtualization is rapidly growing Storage Class Memory, or SCM, is an exciting new in popularity for cloud deployments and applications as a memory technology with the potential of replacing hard virtualization alternative due to the ease of deployment cou- drives and SSDs as it offers high-speed, byte-addressable pled with high-performance. Emerging byte-addressable, non- volatile memories, commonly called Storage Class Memory or persistence on the main memory bus. Several technologies SCM, technologies are promising both byte-addressability and are currently under research and development, each with dif- persistence near DRAM speeds operating on the main memory ferent performance, durability, and capacity characteristics. bus. These new memory alternatives open up a new realm of These include a ReRAM by Micron and Sony, a slower, but applications that no longer have to rely on slow, block-based very large capacity Phase Change Memory or PCM by Mi- persistence, but can rather operate directly on persistent data using ordinary loads and stores through the cache hierarchy cron and others, and a fast, smaller spin-torque ST-MRAM coupled with transaction techniques. by Everspin. High-speed, byte-addressable persistence will However, SCM presents a new challenge for container-based give rise to new applications that no longer have to rely on applications, which typically access persistent data through slow, block based storage devices and to serialize data for layers of block based file isolation. -
Container and Kernel-Based Virtual Machine (KVM) Virtualization for Network Function Virtualization (NFV)
Container and Kernel-Based Virtual Machine (KVM) Virtualization for Network Function Virtualization (NFV) White Paper August 2015 Order Number: 332860-001US YouLegal Lines andmay Disclaimers not use or facilitate the use of this document in connection with any infringement or other legal analysis concerning Intel products described herein. You agree to grant Intel a non-exclusive, royalty-free license to any patent claim thereafter drafted which includes subject matter disclosed herein. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps. The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Copies of documents which have an order number and are referenced in this document may be obtained by calling 1-800-548-4725 or by visiting: http://www.intel.com/ design/literature.htm. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at http:// www.intel.com/ or from the OEM or retailer. Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks. Tests document performance of components on a particular test, in specific systems. -
Containerization Introduction to Containers, Docker and Kubernetes
Containerization Introduction to Containers, Docker and Kubernetes EECS 768 Apoorv Ingle [email protected] Containers • Containers – lightweight VM or chroot on steroids • Feels like a virtual machine • Get a shell • Install packages • Run applications • Run services • But not really • Uses host kernel • Cannot boot OS • Does not need PID 1 • Process visible to host machine Containers • VM vs Containers Containers • Container Anatomy • cgroup: limit the use of resources • namespace: limit what processes can see (hence use) Containers • cgroup • Resource metering and limiting • CPU • IO • Network • etc.. • $ ls /sys/fs/cgroup Containers • Separate Hierarchies for each resource subsystem (CPU, IO, etc.) • Each process belongs to exactly 1 node • Node is a group of processes • Share resource Containers • CPU cgroup • Keeps track • user/system CPU • Usage per CPU • Can set weights • CPUset cgroup • Reserve to CPU to specific applications • Avoids context switch overheads • Useful for non uniform memory access (NUMA) Containers • Memory cgroup • Tracks pages used by each group • Pages can be shared across groups • Pages “charged” to a group • Shared pages “split the cost” • Set limits on usage Containers • Namespaces • Provides a view of the system to process • Controls what a process can see • Multiple namespaces • pid • net • mnt • uts • ipc • usr Containers • PID namespace • Processes within a PID namespace see only process in the same namespace • Each PID namespace has its own numbering staring from 1 • Namespace is killed when PID 1 goes away -
Control Groups (Cgroups)
System Programming for Linux Containers Control Groups (cgroups) Michael Kerrisk, man7.org © 2020 [email protected] February 2020 Outline 19 Cgroups 19-1 19.1 Introduction to cgroups v1 and v2 19-3 19.2 Cgroups v1: hierarchies and controllers 19-17 19.3 Cgroups v1: populating a cgroup 19-24 19.4 Cgroups v1: release notification 19-33 19.5 Cgroups v1: a survey of the controllers 19-43 19.6 Cgroups /procfiles 19-65 19.7 Cgroup namespaces 19-68 Outline 19 Cgroups 19-1 19.1 Introduction to cgroups v1 and v2 19-3 19.2 Cgroups v1: hierarchies and controllers 19-17 19.3 Cgroups v1: populating a cgroup 19-24 19.4 Cgroups v1: release notification 19-33 19.5 Cgroups v1: a survey of the controllers 19-43 19.6 Cgroups /procfiles 19-65 19.7 Cgroup namespaces 19-68 Goals Cgroups is a big topic Many controllers V1 versus V2 interfaces Our goal: understand fundamental semantics of cgroup filesystem and interfaces Useful from a programming perspective How do I build container frameworks? What else can I build with cgroups? And useful from a system engineering perspective What’s going on underneath my container’s hood? System Programming for Linux Containers ©2020, Michael Kerrisk Cgroups 19-4 §19.1 Focus We’ll focus on: General principles of operation; goals of cgroups The cgroup filesystem Interacting with the cgroup filesystem using shell commands Problems with cgroups v1, motivations for cgroups v2 Differences between cgroups v1 and v2 We’ll look briefly at some of the controllers System Programming for Linux Containers ©2020, Michael Kerrisk Cgroups 19-5 §19.1 -
A Container-Based Approach to OS Specialization for Exascale Computing
A Container-Based Approach to OS Specialization for Exascale Computing Judicael A. Zounmevo∗, Swann Perarnau∗, Kamil Iskra∗, Kazutomo Yoshii∗, Roberto Gioiosay, Brian C. Van Essenz, Maya B. Gokhalez, Edgar A. Leonz ∗Argonne National Laboratory fjzounmevo@, perarnau@mcs., iskra@mcs., [email protected] yPacific Northwest National Laboratory. [email protected] zLawrence Livermore National Laboratory. fvanessen1, maya, [email protected] Abstract—Future exascale systems will impose several con- view is essential for scalability, enabling compute nodes to flicting challenges on the operating system (OS) running on manage and optimize massive intranode thread and task par- the compute nodes of such machines. On the one hand, the allelism and adapt to new memory technologies. In addition, targeted extreme scale requires the kind of high resource usage efficiency that is best provided by lightweight OSes. At the Argo introduces the idea of “enclaves,” a set of resources same time, substantial changes in hardware are expected for dedicated to a particular service and capable of introspection exascale systems. Compute nodes are expected to host a mix of and autonomic response. Enclaves will be able to change the general-purpose and special-purpose processors or accelerators system configuration of nodes and the allocation of power tailored for serial, parallel, compute-intensive, or I/O-intensive to different nodes or to migrate data or computations from workloads. Similarly, the deeper and more complex memory hierarchy will expose multiple coherence domains and NUMA one node to another. They will be used to demonstrate nodes in addition to incorporating nonvolatile RAM. That the support of different levels of fault tolerance—a key expected workload and hardware heterogeneity and complexity concern of exascale systems—with some enclaves handling is not compatible with the simplicity that characterizes high node failures by means of global restart and other enclaves performance lightweight kernels. -
Cgroups Py: Using Linux Control Groups and Systemd to Manage CPU Time and Memory
cgroups py: Using Linux Control Groups and Systemd to Manage CPU Time and Memory Curtis Maves Jason St. John Research Computing Research Computing Purdue University Purdue University West Lafayette, Indiana, USA West Lafayette, Indiana, USA [email protected] [email protected] Abstract—cgroups provide a mechanism to limit user and individual resource. 1 Each directory in a cgroupsfs hierarchy process resource consumption on Linux systems. This paper represents a cgroup. Subdirectories of a directory represent discusses cgroups py, a Python script that runs as a systemd child cgroups of a parent cgroup. Within each directory of service that dynamically throttles users on shared resource systems, such as HPC cluster front-ends. This is done using the the cgroups tree, various files are exposed that allow for cgroups kernel API and systemd. the control of the cgroup from userspace via writes, and the Index Terms—throttling, cgroups, systemd obtainment of statistics about the cgroup via reads [1]. B. Systemd and cgroups I. INTRODUCTION systemd A frequent problem on any shared resource with many users Systemd uses a named cgroup tree (called )—that is that a few users may over consume memory and CPU time. does not impose any resource limits—to manage processes When these resources are exhausted, other users have degraded because it provides a convenient way to organize processes in access to the system. A mechanism is needed to prevent this. a hierarchical structure. At the top of the hierarchy, systemd creates three cgroups By placing throttles on physical memory consumption and [2]: CPU time for each individual user, cgroups py prevents re- source exhaustion on a shared system and ensures continued • system.slice: This contains every non-user systemd access for other users. -
Ubuntu Server Guide Basic Installation Preparing to Install
Ubuntu Server Guide Welcome to the Ubuntu Server Guide! This site includes information on using Ubuntu Server for the latest LTS release, Ubuntu 20.04 LTS (Focal Fossa). For an offline version as well as versions for previous releases see below. Improving the Documentation If you find any errors or have suggestions for improvements to pages, please use the link at thebottomof each topic titled: “Help improve this document in the forum.” This link will take you to the Server Discourse forum for the specific page you are viewing. There you can share your comments or let us know aboutbugs with any page. PDFs and Previous Releases Below are links to the previous Ubuntu Server release server guides as well as an offline copy of the current version of this site: Ubuntu 20.04 LTS (Focal Fossa): PDF Ubuntu 18.04 LTS (Bionic Beaver): Web and PDF Ubuntu 16.04 LTS (Xenial Xerus): Web and PDF Support There are a couple of different ways that the Ubuntu Server edition is supported: commercial support and community support. The main commercial support (and development funding) is available from Canonical, Ltd. They supply reasonably- priced support contracts on a per desktop or per-server basis. For more information see the Ubuntu Advantage page. Community support is also provided by dedicated individuals and companies that wish to make Ubuntu the best distribution possible. Support is provided through multiple mailing lists, IRC channels, forums, blogs, wikis, etc. The large amount of information available can be overwhelming, but a good search engine query can usually provide an answer to your questions.