Linux on System Z - Disk I/O Performance – Part 1
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Red Hat Enterprise Linux 7 7.1 Release Notes
Red Hat Enterprise Linux 7 7.1 Release Notes Release Notes for Red Hat Enterprise Linux 7 Red Hat Customer Content Services Red Hat Enterprise Linux 7 7.1 Release Notes Release Notes for Red Hat Enterprise Linux 7 Red Hat Customer Content Services Legal Notice Copyright © 2015 Red Hat, Inc. This document is licensed by Red Hat under the Creative Commons Attribution-ShareAlike 3.0 Unported License. If you distribute this document, or a modified version of it, you must provide attribution to Red Hat, Inc. and provide a link to the original. If the document is modified, all Red Hat trademarks must be removed. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, JBoss, MetaMatrix, Fedora, the Infinity Logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. Red Hat Software Collections is not formally related to or endorsed by the official Joyent Node.js open source or commercial project. -
Kernel Boot-Time Tracing
Kernel Boot-time Tracing Linux Plumbers Conference 2019 - Tracing Track Masami Hiramatsu <[email protected]> Linaro, Ltd. Speaker Masami Hiramatsu - Working for Linaro and Linaro members - Tech Lead for a Landing team - Maintainer of Kprobes and related tracing features/tools Why Kernel Boot-time Tracing? Debug and analyze boot time errors and performance issues - Measure performance statistics of kernel boot - Analyze driver init failure - Debug boot up process - Continuously tracing from boot time etc. What We Have There are already many ftrace options on kernel command line ● Setup options (trace_options=) ● Output to printk (tp_printk) ● Enable events (trace_events=) ● Enable tracers (ftrace=) ● Filtering (ftrace_filter=,ftrace_notrace=,ftrace_graph_filter=,ftrace_graph_notrace=) ● Add kprobe events (kprobe_events=) ● And other options (alloc_snapshot, traceoff_on_warning, ...) See Documentation/admin-guide/kernel-parameters.txt Example of Kernel Cmdline Parameters In grub.conf linux /boot/vmlinuz-5.1 root=UUID=5a026bbb-6a58-4c23-9814-5b1c99b82338 ro quiet splash tp_printk trace_options=”sym-addr” trace_clock=global ftrace_dump_on_oops trace_buf_size=1M trace_event=”initcall:*,irq:*,exceptions:*” kprobe_event=”p:kprobes/myevent foofunction $arg1 $arg2;p:kprobes/myevent2 barfunction %ax” What Issues? Size limitation ● kernel cmdline size is small (< 256bytes) ● A half of the cmdline is used for normal boot Only partial features supported ● ftrace has too complex features for single command line ● per-event filters/actions, instances, histograms. Solutions? 1. Use initramfs - Too late for kernel boot time tracing 2. Expand kernel cmdline - It is not easy to write down complex tracing options on bootloader (Single line options is too simple) 3. Reuse structured boot time data (Devicetree) - Well documented, structured data -> V1 & V2 series based on this. Boot-time Trace: V1 and V2 series V1 and V2 series posted at June. -
SUSE BU Presentation Template 2014
TUT7317 A Practical Deep Dive for Running High-End, Enterprise Applications on SUSE Linux Holger Zecha Senior Architect REALTECH AG [email protected] Table of Content • About REALTECH • About this session • Design principles • Different layers which need to be considered 2 Table of Content • About REALTECH • About this session • Design principles • Different layers which need to be considered 3 About REALTECH 1/2 REALTECH Software REALTECH Consulting . Business Service Management . SAP Mobile . Service Operations Management . Cloud Computing . Configuration Management and CMDB . SAP HANA . IT Infrastructure Management . SAP Solution Manager . Change Management for SAP . IT Technology . Virtualization . IT Infrastructure 4 About REALTECH 2/2 5 Our Customers Manufacturing IT services Healthcare Media Utilities Consumer Automotive Logistics products Finance Retail REALTECH Consulting GmbH 6 Table of Content • About REALTECH • About this session • Design principles • Different layers which need to be considered 7 The Inspiration for this Session • Several performance workshops at customers • Performance escalations at customer who migrated from UNIX (AIX, Solaris, HP-UX) to Linux • Presenting the experiences made at these customers in this session • Preventing the audience from performance degradation caused from: – Significant design mistakes – Wrong architecture assumptions – Having no architecture at all 8 Performance Optimization The False Estimation Upgrading server with CPUs that are 12.5% faster does not improve application -
Linux Perf Event Features and Overhead
Linux perf event Features and Overhead 2013 FastPath Workshop Vince Weaver http://www.eece.maine.edu/∼vweaver [email protected] 21 April 2013 Performance Counters and Workload Optimized Systems • With processor speeds constant, cannot depend on Moore's Law to deliver increased performance • Code analysis and optimization can provide speedups in existing code on existing hardware • Systems with a single workload are best target for cross- stack hardware/kernel/application optimization • Hardware performance counters are the perfect tool for this type of optimization 1 Some Uses of Performance Counters • Traditional analysis and optimization • Finding architectural reasons for slowdown • Validating Simulators • Auto-tuning • Operating System optimization • Estimating power/energy in software 2 Linux and Performance Counters • Linux has become the operating system of choice in many domains • Runs most of the Top500 list (over 90%) on down to embedded devices (Android Phones) • Until recently had no easy access to hardware performance counters, limiting code analysis and optimization. 3 Linux Performance Counter History • oprofile { system-wide sampling profiler since 2002 • perfctr { widely used general interface available since 1999, required patching kernel • perfmon2 { another general interface, included in kernel for itanium, made generic, big push for kernel inclusion 4 Linux perf event • Developed in response to perfmon2 by Molnar and Gleixner in 2009 • Merged in 2.6.31 as \PCL" • Unusual design pushes most functionality into kernel -
Speeding up Linux Disk Encryption Ignat Korchagin @Ignatkn $ Whoami
Speeding Up Linux Disk Encryption Ignat Korchagin @ignatkn $ whoami ● Performance and security at Cloudflare ● Passionate about security and crypto ● Enjoy low level programming @ignatkn Encrypting data at rest The storage stack applications @ignatkn The storage stack applications filesystems @ignatkn The storage stack applications filesystems block subsystem @ignatkn The storage stack applications filesystems block subsystem storage hardware @ignatkn Encryption at rest layers applications filesystems block subsystem SED, OPAL storage hardware @ignatkn Encryption at rest layers applications filesystems LUKS/dm-crypt, BitLocker, FileVault block subsystem SED, OPAL storage hardware @ignatkn Encryption at rest layers applications ecryptfs, ext4 encryption or fscrypt filesystems LUKS/dm-crypt, BitLocker, FileVault block subsystem SED, OPAL storage hardware @ignatkn Encryption at rest layers DBMS, PGP, OpenSSL, Themis applications ecryptfs, ext4 encryption or fscrypt filesystems LUKS/dm-crypt, BitLocker, FileVault block subsystem SED, OPAL storage hardware @ignatkn Storage hardware encryption Pros: ● it’s there ● little configuration needed ● fully transparent to applications ● usually faster than other layers @ignatkn Storage hardware encryption Pros: ● it’s there ● little configuration needed ● fully transparent to applications ● usually faster than other layers Cons: ● no visibility into the implementation ● no auditability ● sometimes poor security https://support.microsoft.com/en-us/help/4516071/windows-10-update-kb4516071 @ignatkn Block -
Linux on IBM Z
Linux on IBM Z Pervasive Encryption with Linux on IBM Z: from a performance perspective Danijel Soldo Software Performance Analyst Linux on IBM Z Performance Evaluation _ [email protected] IBM Z / Danijel Soldo – Pervasive Encryption with Linux on IBM Z: from a performance perspective / © 2018 IBM Corporation Notices and disclaimers • © 2018 International Business Machines Corporation. No part of • Performance data contained herein was generally obtained in a this document may be reproduced or transmitted in any form controlled, isolated environments. Customer examples are without written permission from IBM. presented as illustrations of how those • U.S. Government Users Restricted Rights — use, duplication • customers have used IBM products and the results they may have or disclosure restricted by GSA ADP Schedule Contract with achieved. Actual performance, cost, savings or other results in IBM. other operating environments may vary. • Information in these presentations (including information relating • References in this document to IBM products, programs, or to products that have not yet been announced by IBM) has been services does not imply that IBM intends to make such products, reviewed for accuracy as of the date of initial publication programs or services available in all countries in which and could include unintentional technical or typographical IBM operates or does business. errors. IBM shall have no responsibility to update this information. This document is distributed “as is” without any warranty, • Workshops, sessions and associated materials may have been either express or implied. In no event, shall IBM be liable for prepared by independent session speakers, and do not necessarily any damage arising from the use of this information, reflect the views of IBM. -
On the Performance Variation in Modern Storage Stacks
On the Performance Variation in Modern Storage Stacks Zhen Cao1, Vasily Tarasov2, Hari Prasath Raman1, Dean Hildebrand2, and Erez Zadok1 1Stony Brook University and 2IBM Research—Almaden Appears in the proceedings of the 15th USENIX Conference on File and Storage Technologies (FAST’17) Abstract tions on different machines have to compete for heavily shared resources, such as network switches [9]. Ensuring stable performance for storage stacks is im- In this paper we focus on characterizing and analyz- portant, especially with the growth in popularity of ing performance variations arising from benchmarking hosted services where customers expect QoS guaran- a typical modern storage stack that consists of a file tees. The same requirement arises from benchmarking system, a block layer, and storage hardware. Storage settings as well. One would expect that repeated, care- stacks have been proven to be a critical contributor to fully controlled experiments might yield nearly identi- performance variation [18, 33, 40]. Furthermore, among cal performance results—but we found otherwise. We all system components, the storage stack is the corner- therefore undertook a study to characterize the amount stone of data-intensive applications, which become in- of variability in benchmarking modern storage stacks. In creasingly more important in the big data era [8, 21]. this paper we report on the techniques used and the re- Although our main focus here is reporting and analyz- sults of this study. We conducted many experiments us- ing the variations in benchmarking processes, we believe ing several popular workloads, file systems, and storage that our observations pave the way for understanding sta- devices—and varied many parameters across the entire bility issues in production systems. -
Linux Performance Tools
Linux Performance Tools Brendan Gregg Senior Performance Architect Performance Engineering Team [email protected] @brendangregg This Tutorial • A tour of many Linux performance tools – To show you what can be done – With guidance for how to do it • This includes objectives, discussion, live demos – See the video of this tutorial Observability Benchmarking Tuning Stac Tuning • Massive AWS EC2 Linux cloud – 10s of thousands of cloud instances • FreeBSD for content delivery – ~33% of US Internet traffic at night • Over 50M subscribers – Recently launched in ANZ • Use Linux server tools as needed – After cloud monitoring (Atlas, etc.) and instance monitoring (Vector) tools Agenda • Methodologies • Tools • Tool Types: – Observability – Benchmarking – Tuning – Static • Profiling • Tracing Methodologies Methodologies • Objectives: – Recognize the Streetlight Anti-Method – Perform the Workload Characterization Method – Perform the USE Method – Learn how to start with the questions, before using tools – Be aware of other methodologies My system is slow… DEMO & DISCUSSION Methodologies • There are dozens of performance tools for Linux – Packages: sysstat, procps, coreutils, … – Commercial products • Methodologies can provide guidance for choosing and using tools effectively • A starting point, a process, and an ending point An#-Methodologies • The lack of a deliberate methodology… Street Light An<-Method 1. Pick observability tools that are: – Familiar – Found on the Internet – Found at random 2. Run tools 3. Look for obvious issues Drunk Man An<-Method • Tune things at random until the problem goes away Blame Someone Else An<-Method 1. Find a system or environment component you are not responsible for 2. Hypothesize that the issue is with that component 3. Redirect the issue to the responsible team 4. -
Vytvoření Softwareově Definovaného Úložiště Pro Potřeby Ukládání a Sdílení Dat V Rámci Instituce a Jeho Zálohování Do Datových Úložišť Cesnet
Slezská univerzita v Opavě Centrum informačních technologií Vysoká škola báňská – Technická univerzita Ostrava Fakulta elektrotechniky a informatiky Technická zpráva k projektu 529R1/2014 Vytvoření softwareově definovaného úložiště pro potřeby ukládání a sdílení dat v rámci instituce a jeho zálohování do datových úložišť Cesnet Řešitel: Ing. Jiří Sléžka Spoluřešitelé: Mgr. Jan Nosek, Ing. Pavel Nevlud, Ing. Marek Dvorský, Ph.D., Ing. Jiří Vychodil, Ing. Lukáš Kapičák Leden 2016 Obsah 1 Popis projektu...................................................................................................................................1 2 Cíle projektu.....................................................................................................................................1 3 Volba HW a SW komponent.............................................................................................................1 3.1 Hardware...................................................................................................................................1 3.2 GlusterFS..................................................................................................................................2 3.2.1 Replicated GlusterFS Volume...........................................................................................2 3.2.2 Distributed GlusterFS Volume..........................................................................................3 3.2.3 Striped Glusterfs Volume..................................................................................................3 -
DM-Relay - Safe Laptop Mode Via Linux Device Mapper
' $ DM-Relay - Safe Laptop Mode via Linux Device Mapper Study Thesis by cand. inform. Fabian Franz at the Faculty of Informatics Supervisor: Prof. Dr. Frank Bellosa Supervising Research Assistant: Dipl.-Inform. Konrad Miller Day of completion: 04/05/2010 &KIT – Universitat¨ des Landes Baden-Wurttemberg¨ und nationales Forschungszentrum in der Helmholtz-Gemeinschaft www.kit.edu % I hereby declare that this thesis is my own original work which I created without illegitimate help by others, that I have not used any other sources or resources than the ones indicated and that due acknowledgment is given where reference is made to the work of others. Karlsruhe, April 5th, 2010 Contents Deutsche Zusammenfassung xi 1 Introduction 1 1.1 Problem Definition . .1 1.2 Objectives . .1 1.3 Methodology . .1 1.4 Contribution . .2 1.5 Thesis Outline . .2 2 Background 3 2.1 Problems of Disk Power Management . .3 2.2 State of the Art . .4 2.3 Summary of this chapter . .8 3 Analysis 9 3.1 Pro and Contra . .9 3.2 A new approach . 13 3.3 Analysis of Proposal . 15 3.4 Summary of this chapter . 17 4 Design 19 4.1 Common problems . 19 4.2 System-Design . 21 4.3 Summary of this chapter . 21 5 Implementation of a dm-module for the Linux kernel 23 5.1 System-Architecture . 24 5.2 Log suitable for Flash-Storage . 28 5.3 Using dm-relay in practice . 31 5.4 Summary of this chapter . 31 vi Contents 6 Evaluation 33 6.1 Methodology . 33 6.2 Benchmarking setup . -
On Access Control Model of Linux Native Performance Monitoring Motivation
On access control model of Linux native performance monitoring Motivation • socialize Perf access control management to broader community • promote the management to security sensitive production environments • discover demand on extensions to the existing Perf access control model 2 Model overview • Subjects: processes • Access control: • superuser root • LSM hooks for MAC (e.g. SELinux) subjects • privileged user groups • Linux capabilities (DAC) • unprivileged users • perf_event_paranoid sysctl access and • Objects: telemetry data • Resource control: • tracepoints, OS events resource control • CPU time: sample rate & throttling • CPU • Memory: perf_events_mlock_kb sysctl • Uncore Objects • Other HW • File descriptors: ulimit -n (RLIMIT_NOFILE) system • Scope • Level user cgroups • process • user mode • cgroups • kernel kernel • system process • hypervisor hypervisor 3 Subjects Users • root, superuser: • euid = 0 and/or CAP_SYS_ADMIN • unprivileged users: • perf_event_paranoid sysctl • Perf privileged user group: -rwxr-x--- 2 root perf_users 11M Oct 19 15:12 perf # getcap perf perf = cap_perfmon,…=ep root unprivileged Perf users 4 Telemetry, scope, level Telemetr Objects: SW, HW telemetry data y Uncore • tracepoints, OS events, eBPF • CPUs events and related HW CPU • Uncore events (LLC, Interconnect, DRAM) SW events • Other (e.g. FPGA) process cgroup user Scope: Level: system kernel • process • user hypervisor • cgroups • kernel Scope Leve • system wide • hypervisor l 5 perf: interrupt took too long (3000 > 2000), lowering kernel.perf_event_max_sample_rate -
A Hybrid Swapping Scheme Based on Per-Process Reclaim for Performance Improvement of Android Smartphones (August 2018)
Received August 19, 2018, accepted September 14, 2018, date of publication October 1, 2018, date of current version October 25, 2018. Digital Object Identifier 10.1109/ACCESS.2018.2872794 A Hybrid Swapping Scheme Based On Per-Process Reclaim for Performance Improvement of Android Smartphones (August 2018) JUNYEONG HAN 1, SUNGEUN KIM1, SUNGYOUNG LEE1, JAEHWAN LEE2, AND SUNG JO KIM2 1LG Electronics, Seoul 07336, South Korea 2School of Software, Chung-Ang University, Seoul 06974, South Korea Corresponding author: Sung Jo Kim ([email protected]) This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant 2016R1D1A1B03931004 and in part by the Chung-Ang University Research Scholarship Grants in 2015. ABSTRACT As a way to increase the actual main memory capacity of Android smartphones, most of them make use of zRAM swapping, but it has limitation in increasing its capacity since it utilizes main memory. Unfortunately, they cannot use secondary storage as a swap space due to the long response time and wear-out problem. In this paper, we propose a hybrid swapping scheme based on per-process reclaim that supports both secondary-storage swapping and zRAM swapping. It attempts to swap out all the pages in the working set of a process to a zRAM swap space rather than killing the process selected by a low-memory killer, and to swap out the least recently used pages into a secondary storage swap space. The main reason being is that frequently swap- in/out pages use the zRAM swap space while less frequently swap-in/out pages use the secondary storage swap space, in order to reduce the page operation cost.