Comparison of Virtualization Performance: Vmware and KVM
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Bull SAS: Novascale B260 (Intel Xeon Processor 5110,1.60Ghz)
SPEC CINT2006 Result spec Copyright 2006-2014 Standard Performance Evaluation Corporation Bull SAS SPECint2006 = 10.2 NovaScale B260 (Intel Xeon processor 5110,1.60GHz) SPECint_base2006 = 9.84 CPU2006 license: 20 Test date: Dec-2006 Test sponsor: Bull SAS Hardware Availability: Dec-2006 Tested by: Bull SAS Software Availability: Dec-2006 0 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 12.7 400.perlbench 11.6 8.64 401.bzip2 8.41 6.59 403.gcc 6.38 11.9 429.mcf 12.7 12.0 445.gobmk 10.6 6.90 456.hmmer 6.72 10.8 458.sjeng 9.90 11.0 462.libquantum 10.8 17.1 464.h264ref 16.8 9.22 471.omnetpp 8.38 7.84 473.astar 7.83 12.5 483.xalancbmk 12.4 SPECint_base2006 = 9.84 SPECint2006 = 10.2 Hardware Software CPU Name: Intel Xeon 5110 Operating System: Windows Server 2003 Enterprise Edition (32 bits) CPU Characteristics: 1.60 GHz, 4MB L2, 1066MHz bus Service Pack1 CPU MHz: 1600 Compiler: Intel C++ Compiler for IA32 version 9.1 Package ID W_CC_C_9.1.033 Build no 20061103Z FPU: Integrated Microsoft Visual Studio .NET 2003 (lib & linker) CPU(s) enabled: 1 core, 1 chip, 2 cores/chip MicroQuill SmartHeap Library 8.0 (shlW32M.lib) CPU(s) orderable: 1 to 2 chips Auto Parallel: No Primary Cache: 32 KB I + 32 KB D on chip per core File System: NTFS Secondary Cache: 4 MB I+D on chip per chip System State: Default L3 Cache: None Base Pointers: 32-bit Other Cache: None Peak Pointers: 32-bit Memory: 8 GB (2GB DIMMx4, FB-DIMM PC2-5300F ECC CL5) Other Software: None Disk Subsystem: 73 GB SAS, 10000RPM Other Hardware: None -
Virtualization Getting Started Guide
Red Hat Enterprise Linux 7 Virtualization Getting Started Guide Introduction to virtualization technologies available with RHEL Last Updated: 2020-02-24 Red Hat Enterprise Linux 7 Virtualization Getting Started Guide Introduction to virtualization technologies available with RHEL Jiri Herrmann Red Hat Customer Content Services [email protected] Yehuda Zimmerman Red Hat Customer Content Services [email protected] Dayle Parker Red Hat Customer Content Services Laura Novich Red Hat Customer Content Services Jacquelynn East Red Hat Customer Content Services Scott Radvan Red Hat Customer Content Services Legal Notice Copyright © 2019 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, the Red Hat logo, JBoss, OpenShift, 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. -
Microbenchmarks in Big Data
M Microbenchmark Overview Microbenchmarks constitute the first line of per- Nicolas Poggi formance testing. Through them, we can ensure Databricks Inc., Amsterdam, NL, BarcelonaTech the proper and timely functioning of the different (UPC), Barcelona, Spain individual components that make up our system. The term micro, of course, depends on the prob- lem size. In BigData we broaden the concept Synonyms to cover the testing of large-scale distributed systems and computing frameworks. This chap- Component benchmark; Functional benchmark; ter presents the historical background, evolution, Test central ideas, and current key applications of the field concerning BigData. Definition Historical Background A microbenchmark is either a program or routine to measure and test the performance of a single Origins and CPU-Oriented Benchmarks component or task. Microbenchmarks are used to Microbenchmarks are closer to both hardware measure simple and well-defined quantities such and software testing than to competitive bench- as elapsed time, rate of operations, bandwidth, marking, opposed to application-level – macro or latency. Typically, microbenchmarks were as- – benchmarking. For this reason, we can trace sociated with the testing of individual software microbenchmarking influence to the hardware subroutines or lower-level hardware components testing discipline as can be found in Sumne such as the CPU and for a short period of time. (1974). Furthermore, we can also find influence However, in the BigData scope, the term mi- in the origins of software testing methodology crobenchmarking is broadened to include the during the 1970s, including works such cluster – group of networked computers – acting as Chow (1978). One of the first examples of as a single system, as well as the testing of a microbenchmark clearly distinguishable from frameworks, algorithms, logical and distributed software testing is the Whetstone benchmark components, for a longer period and larger data developed during the late 1960s and published sizes. -
Static Vulnerability Analysis of Docker Images
DEGREE PROJECT FOR MASTER OF SCIENCE IN ENGINEERING COMPUTER SECURITY Static Vulnerability Analysis of Docker Images Michael Falk | Oscar Henriksson Blekinge Institute of Technology, Karlskrona, Sweden, 2017 Supervisor: Emiliano Casalicchio, Department of Computer Science and Engineering, BTH Abstract Docker is a popular tool for virtualization that allows for fast and easy deployment of applications and has been growing increasingly popular among companies. Docker also include a large library of images from the repository Docker Hub which mainly is user created and uncontrolled. This leads to low frequency of updates which results in vulnerabilities in the images. In this thesis we are developing a tool for determining what vulnerabilities that exists inside Docker images with a Linux distribution. This is done by using our own tool for downloading and retrieving the necessary data from the images and then utilizing Outpost24’s scanner for finding vulnerabilities in Linux packages. With the help of this tool we also publish statistics of vulnerabilities from the top downloaded images of Docker Hub. The result is a tool that can successfully scan a Docker image for vulnerabilities in certain Linux distributions. From a survey over the top 1000 Docker images it has also been shown that the amount of vulnerabilities have increased in comparison to earlier surveys of Docker images. Keywords: Docker, Containerization, Vulnerability analysis, Vulnerability scanning i Sammanfattning Docker är ett populärt verktyg för virtualisering som används för att snabbt och enkelt sätta upp applikationer och har vuxit sig populärt bland företag. Docker inkluderar även ett stort bibliotek av images från datakatalogen Docker Hub vilket huvudsakligen består av användarskapat och okontrollerat innehåll. -
Overview of the SPEC Benchmarks
9 Overview of the SPEC Benchmarks Kaivalya M. Dixit IBM Corporation “The reputation of current benchmarketing claims regarding system performance is on par with the promises made by politicians during elections.” Standard Performance Evaluation Corporation (SPEC) was founded in October, 1988, by Apollo, Hewlett-Packard,MIPS Computer Systems and SUN Microsystems in cooperation with E. E. Times. SPEC is a nonprofit consortium of 22 major computer vendors whose common goals are “to provide the industry with a realistic yardstick to measure the performance of advanced computer systems” and to educate consumers about the performance of vendors’ products. SPEC creates, maintains, distributes, and endorses a standardized set of application-oriented programs to be used as benchmarks. 489 490 CHAPTER 9 Overview of the SPEC Benchmarks 9.1 Historical Perspective Traditional benchmarks have failed to characterize the system performance of modern computer systems. Some of those benchmarks measure component-level performance, and some of the measurements are routinely published as system performance. Historically, vendors have characterized the performances of their systems in a variety of confusing metrics. In part, the confusion is due to a lack of credible performance information, agreement, and leadership among competing vendors. Many vendors characterize system performance in millions of instructions per second (MIPS) and millions of floating-point operations per second (MFLOPS). All instructions, however, are not equal. Since CISC machine instructions usually accomplish a lot more than those of RISC machines, comparing the instructions of a CISC machine and a RISC machine is similar to comparing Latin and Greek. 9.1.1 Simple CPU Benchmarks Truth in benchmarking is an oxymoron because vendors use benchmarks for marketing purposes. -
Comparing Filesystem Performance: Red Hat Enterprise Linux 6 Vs
COMPARING FILE SYSTEM I/O PERFORMANCE: RED HAT ENTERPRISE LINUX 6 VS. MICROSOFT WINDOWS SERVER 2012 When choosing an operating system platform for your servers, you should know what I/O performance to expect from the operating system and file systems you select. In the Principled Technologies labs, using the IOzone file system benchmark, we compared the I/O performance of two operating systems and file system pairs, Red Hat Enterprise Linux 6 with ext4 and XFS file systems, and Microsoft Windows Server 2012 with NTFS and ReFS file systems. Our testing compared out-of-the-box configurations for each operating system, as well as tuned configurations optimized for better performance, to demonstrate how a few simple adjustments can elevate I/O performance of a file system. We found that file systems available with Red Hat Enterprise Linux 6 delivered better I/O performance than those shipped with Windows Server 2012, in both out-of- the-box and optimized configurations. With I/O performance playing such a critical role in most business applications, selecting the right file system and operating system combination is critical to help you achieve your hardware’s maximum potential. APRIL 2013 A PRINCIPLED TECHNOLOGIES TEST REPORT Commissioned by Red Hat, Inc. About file system and platform configurations While you can use IOzone to gauge disk performance, we concentrated on the file system performance of two operating systems (OSs): Red Hat Enterprise Linux 6, where we examined the ext4 and XFS file systems, and Microsoft Windows Server 2012 Datacenter Edition, where we examined NTFS and ReFS file systems. -
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. -
Towards Better Performance Per Watt in Virtual Environments on Asymmetric Single-ISA Multi-Core Systems
Towards Better Performance Per Watt in Virtual Environments on Asymmetric Single-ISA Multi-core Systems Viren Kumar Alexandra Fedorova Simon Fraser University Simon Fraser University 8888 University Dr 8888 University Dr Vancouver, Canada Vancouver, Canada [email protected] [email protected] ABSTRACT performance per watt than homogeneous multicore proces- Single-ISA heterogeneous multicore architectures promise to sors. As power consumption in data centers becomes a grow- deliver plenty of cores with varying complexity, speed and ing concern [3], deploying ASISA multicore systems is an performance in the near future. Virtualization enables mul- increasingly attractive opportunity. These systems perform tiple operating systems to run concurrently as distinct, in- at their best if application workloads are assigned to het- dependent guest domains, thereby reducing core idle time erogeneous cores in consideration of their runtime proper- and maximizing throughput. This paper seeks to identify a ties [4][13][12][18][24][21]. Therefore, understanding how to heuristic that can aid in intelligently scheduling these vir- schedule data-center workloads on ASISA systems is an im- tualized workloads to maximize performance while reducing portant problem. This paper takes the first step towards power consumption. understanding the properties of data center workloads that determine how they should be scheduled on ASISA multi- We propose that the controlling domain in a Virtual Ma- core processors. Since virtual machine technology is a de chine Monitor or hypervisor is relatively insensitive to changes facto standard for data centers, we study virtual machine in core frequency, and thus scheduling it on a slower core (VM) workloads. saves power while only slightly affecting guest domain per- formance. -
Erlang on Physical Machine
on $ whoami Name: Zvi Avraham E-mail: [email protected] /ˈkɒm. pɑː(ɹ)t. mɛntl̩. aɪˌzeɪ. ʃən/ Physicalization • The opposite of Virtualization • dedicated machines • no virtualization overhead • no noisy neighbors – nobody stealing your CPU cycles, IOPS or bandwidth – your EC2 instance may have a Netflix “roommate” ;) • Mostly used by ARM-based public clouds • also called Bare Metal or HPC clouds Sandbox – a virtual container in which untrusted code can be safely run Sandbox examples: ZeroVM & AWS Lambda based on Google Native Client: A Sandbox for Portable, Untrusted x86 Native Code Compartmentalization in terms of Virtualization Physicalization No Virtualization Virtualization HW-level Virtualization Containerization OS-level Virtualization Sandboxing Userspace-level Virtualization* Cloud runs on virtual HW HARDWARE Does the OS on your Cloud instance still supports floppy drive? $ ls /dev on Ubuntu 14.04 AWS EC2 instance • 64 teletype devices? • Sound? • 32 serial ports? • VGA? “It’s DUPLICATED on so many LAYERS” Application + Configuration process* OS Middleware (Spring/OTP) Container Managed Runtime (JVM/BEAM) VM Guest Container OS Container Guest OS Hypervisor Hardware We run Single App per VM APPS We run in Single User mode USERS Minimalistic Linux OSes • Embedded Linux versions • DamnSmall Linux • Linux with BusyBox Min. Linux OSes for Containers JeOS – “Just Enough OS” • CoreOS • RancherOS • RedHat Project Atomic • VMware Photon • Intel Clear Linux • Hyper # of Processes and Threads per OS OSv + CLI RancherOS processes CoreOS threads -
I.MX 8Quadxplus Power and Performance
NXP Semiconductors Document Number: AN12338 Application Note Rev. 4 , 04/2020 i.MX 8QuadXPlus Power and Performance 1. Introduction Contents This application note helps you to design power 1. Introduction ........................................................................ 1 management systems. It illustrates the current drain 2. Overview of i.MX 8QuadXPlus voltage supplies .............. 1 3. Power measurement of the i.MX 8QuadXPlus processor ... 2 measurements of the i.MX 8QuadXPlus Applications 3.1. VCC_SCU_1V8 power ........................................... 4 Processors taken on NXP Multisensory Evaluation Kit 3.2. VCC_DDRIO power ............................................... 4 (MEK) Platform through several use cases. 3.3. VCC_CPU/VCC_GPU/VCC_MAIN power ........... 5 3.4. Temperature measurements .................................... 5 This document provides details on the performance and 3.5. Hardware and software used ................................... 6 3.6. Measuring points on the MEK platform .................. 6 power consumption of the i.MX 8QuadXPlus 4. Use cases and measurement results .................................... 6 processors under a variety of low- and high-power 4.1. Low-power mode power consumption (Key States modes. or ‘KS’)…… ......................................................................... 7 4.2. Complex use case power consumption (Arm Core, The data presented in this application note is based on GPU active) ......................................................................... 11 5. SOC -
Software Performance Engineering Using Virtual Time Program Execution
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Spiral - Imperial College Digital Repository IMPERIAL COLLEGE LONDON DEPARTMENT OF COMPUTING Software Performance Engineering using Virtual Time Program Execution Nikolaos Baltas Submitted in part fullment of the requirements for the degree of Doctor of Philosophy in Computing of Imperial College London and the Diploma of Imperial College London To my mother Dimitra for her endless support The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Re- searchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. 4 Abstract In this thesis we introduce a novel approach to software performance engineering that is based on the execution of code in virtual time. Virtual time execution models the timing-behaviour of unmodified applications by scaling observed method times or replacing them with results acquired from performance model simulation. This facilitates the investigation of \what-if" performance predictions of applications comprising an arbitrary combination of real code and performance models. The ability to analyse code and models in a single framework enables performance testing throughout the software lifecycle, without the need to to extract perfor- mance models from code. This is accomplished by forcing thread scheduling decisions to take into account the hypothetical time-scaling or model-based performance specifications of each method. -
Red Hat Enterprise Virtualization 3.0 Live Chat Transcript Sponsored by Red Hat
Red Hat Enterprise Virtualization 3.0 Live Chat Transcript Sponsored by Red Hat Speakers: Chuck Dubuque, Senior Product Marketing Manager, Red Hat Andy Cathrow, Product Manager, Red Hat Red Hat Virtualization Live Chat Transcript 2.23.12 Joe:Hi Everyone, thanks for joining the Red Hat Live Chat. Joe:Today we have Chuck Dubuque & Andrew Cathrow with the Red Hat Virtualization team available LIVE to answer your questions. Joe:Speaker Bios:Chuck Dubuque is the Senior Product Marketing Manager for Red Hat Enterprise Virtualization and is responsible for market analysis, program strategy, and channel support. Prior to joining Red Hat, he worked for three years at a mid-sized VAR (value-added reseller) where he experienced both the marketing and engineering of enterprise hardware and software, including Red Hat Enterprise Linux, VMware, Microsoft Windows Server, NetApp, IBM, Cisco, and Dell. Earlier in his career, Dubuque spent eight years in the biotechnology space in marketing and business development. He earned an MBA from Stanford Graduate School of Business and a bachelor's degree from Dartmouth College. Andrew Cathrow serves as Product Manager at Red Hat, where he is responsible for Red Hat's virtualization products. He has also managed Red Hat's sales engineers. Prior to joining Red Hat in 2006, Cathrow worked in product management for a configuration company, and also for a software company that developed middleware and messaging mainframe and midrange systems. Earlier in his career, Cathrow held various positions at IBM Global Services. Joe:Please feel free to start asking questions now Chuck:Thanks Joe. First I'd like to remind everyone that Red Hat launched RHEV 3.0 on January 18, and out launch event is now available on-demand at http://bit.ly/rhev3event.