Adaptive Virtual Machine Placement Supporting Performance Slas
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Metrics for Performance Advertisement
Metrics for Performance Advertisement Vojtěch Horký Peter Libič Petr Tůma 2010 – 2021 This work is licensed under a “CC BY-NC-SA 3.0” license. Created to support the Charles University Performance Evaluation lecture. See http://d3s.mff.cuni.cz/teaching/performance-evaluation for details. Contents 1 Overview 1 2 Operation Frequency Related Metrics 2 3 Operation Duration Related Metrics 3 4 Benchmark Workloads 4 1 Overview Performance Advertisement Measuring for the purpose of publishing performance information. Requirements: – Well defined meaning. – Simple to understand. – Difficult to game. Pitfalls: – Publication makes results subject to pressure. – Often too simple to convey meaningful information. – Performance in contemporary computer systems is never simple. Speed Related Metrics Responsiveness Metrics – Time (Task Response Time) – How long does it take to finish the task ? Productivity Metrics – Rate (Task Throughput) – How many tasks can the system complete per time unit ? Utilization Metrics – Resource Use (Utilization) – How much is the system loaded when working on a task ? – Share of time a resource is busy or over given load level. – Helps identify bottlenecks (most utilized resources in the system). 1 Metric for Webmail Performance What metric would you choose to characterize performance of a web mail site ? – User oriented metric would be end-to-end operation time. – Server oriented metric would be request processing time. – How about metrics in between ? – And would you include mail delivery time ? How is throughput related to latency ? How is utilization defined for various resources ? 2 Operation Frequency Related Metrics Clock Rate Clock rate (frequency) of the component (CPU, bus, memory) in MHz. Most often we talk about CPU frequency. -
The Nutanix Design Guide
The FIRST EDITION Nutanix Design Guide Edited by AngeloLuciani,VCP By Nutanix, RenévandenBedem, incollaborationwith NPX,VCDX4,DECM-EA RoundTowerTechnologies Table of Contents 1 Foreword 3 2 Contributors 5 3 Acknowledgements 7 4 Using This Book 7 5 Introduction To Nutanix 9 6 Why Nutanix? 17 7 The Nutanix Eco-System 37 8 Certification & Training 43 9 Design Methodology 49 & The NPX Program 10 Channel Charter 53 11 Mission-Critical Applications 57 12 SAP On Nutanix 69 13 Hardware Platforms 81 14 Sizer & Collector 93 15 IBM Power Systems 97 16 Remote Office, Branch Office 103 17 Xi Frame & EUC 113 Table of Contents 18 Xi IoT 121 19 Xi Leap, Data Protection, 133 DR & Metro Availability 20 Cloud Management 145 & Automation: Calm, Xi Beam & Xi Epoch 21 Era 161 22 Karbon 165 23 Acropolis Security & Flow 169 24 Files 193 25 Volumes 199 26 Buckets 203 27 Prism 209 28 Life Cycle Manager 213 29 AHV 217 30 Move 225 31 X-Ray 229 32 Foundation 241 33 Data Center Facilities 245 34 People & Process 251 35 Risk Management 255 The Nutanix Design Guide 1 Foreword Iamhonoredtowritethisforewordfor‘TheNutanixDesignGuide’. Wehavealwaysbelievedthatcloudwillbemorethanjustarented modelforenterprises.Computingwithintheenterpriseisnuanced, asittriestobalancethefreedomandfriction-freeaccessofthe publiccloudwiththesecurityandcontroloftheprivatecloud.The privateclouditselfisspreadbetweencoredatacenters,remoteand branchoffices,andedgeoperations.Thetrifectaofthe3laws–(a) LawsoftheLand(dataandapplicationsovereignty),(b)Lawsof Physics(dataandmachinegravity),and(c)LawsofEconomics -
Model Driven Scheduling for Virtualized Workloads
Model Driven Scheduling for Virtualized Workloads Proefschrift voorgelegd op 28 juni 2013 tot het behalen van de graad van Doctor in de Wetenschappen – Informatica, bij de faculteit Wetenschappen, aan de Universiteit Antwerpen. PROMOTOREN: prof. dr. Jan Broeckhove dr. Kurt Vanmechelen Sam Verboven RESEARCH GROUP COMPUTATIONAL MODELINGAND PROGRAMMING Dankwoord Het behalen van een doctoraat is een opdracht die zonder hulp onmogelijk tot een goed einde kan gebracht worden. Gelukkig heb ik de voorbije jaren de kans gekregen om samen te werken met talrijke stimulerende collega’s. Stuk voor stuk hebben zij bijgedragen tot mijn professionele en persoonlijke ontwikkeling. Hun geduldige hulp en steun was essentieel bij het overwinnen van de vele uitdagingen die met een doctoraat gepaard gaan. Graag zou ik hier dan ook enkele woorden van dank neerschrijven. Allereerst zou ik graag prof. dr. Jan Broeckhove en em. prof. dr. Frans Arickx bedanken om mij de kans te geven een gevarieerde en boeiend academisch traject te starten. Bij het beginnen van mijn doctoraat heeft Frans mij niet alleen geholpen om een capabel onderzoeker te worden, ook bij het lesgeven heeft hij mij steeds met raad en daad bijgestaan. Na het emiraat van Frans heeft Jan deze begeleiding overgenomen en er voor gezorgd dat ik het begonnen traject ook succesvol kon beeïndigen. Beiden hebben ze mij steeds grote vrijheid gegeven in mijn zoektocht om interessante onderzoeksvragen te identificeren en beantwoorden. Vervolgens zou ik graag dr. Peter Hellinckx en dr. Kurt Vanmechelen bedanken voor hun persoonlijke en vaak intensieve begeleiding. Zelfs voor de start van mijn academische carrière, bij het schrijven van mijn Master thesis, heeft Peter mij klaar- gestoomd voor een vlotte start als onderzoeker. -
“Freedom” Koan-Sin Tan [email protected] OSDC.Tw, Taipei Apr 11Th, 2014
Understanding Android Benchmarks “freedom” koan-sin tan [email protected] OSDC.tw, Taipei Apr 11th, 2014 1 disclaimers • many of the materials used in this slide deck are from the Internet and textbooks, e.g., many of the following materials are from “Computer Architecture: A Quantitative Approach,” 1st ~ 5th ed • opinions expressed here are my personal one, don’t reflect my employer’s view 2 who am i • did some networking and security research before • working for a SoC company, recently on • big.LITTLE scheduling and related stuff • parallel construct evaluation • run benchmarking from time to time • for improving performance of our products, and • know what our colleagues' progress 3 • Focusing on CPU and memory parts of benchmarks • let’s ignore graphics (2d, 3d), storage I/O, etc. 4 Blackbox ! • google image search “benchmark”, you can find many of them are Android-related benchmarks • Similar to recently Cross-Strait Trade in Services Agreement (TiSA), most benchmarks on Android platform are kinda blackbox 5 Is Apple A7 good? • When Apple released the new iPhone 5s, you saw many technical blog showed some benchmarks for reviews they came up • commonly used ones: • GeekBench • JavaScript benchmarks • Some graphics benchmarks • Why? Are they right ones? etc. e.g., http://www.anandtech.com/show/7335/the-iphone-5s-review 6 open blackbox 7 Android Benchmarks 8 http:// www.anandtech.com /show/7384/state-of- cheating-in-android- benchmarks No, not improvement in this way 9 Assuming there is not cheating, what we we can do? Outline • Performance benchmark review • Some Android benchmarks • What we did and what still can be done • Future 11 To quote what Prof. -
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. -
Big Data Benchmarking Workshop Publications
Benchmarking Datacenter and Big Data Systems Wanling Gao, Zhen Jia, Lei Wang, Yuqing Zhu, Chunjie Luo, Yingjie Shi, Yongqiang He, Shiming Gong, Xiaona Li, Shujie Zhang, Bizhu Qiu, Lixin Zhang, Jianfeng Zhan INSTITUTE OFTECHNOLOGY COMPUTING http://prof.ict.ac.cn/ICTBench 1 Acknowledgements This work is supported by the Chinese 973 project (Grant No.2011CB302502), the Hi- Tech Research and Development (863) Program of China (Grant No.2011AA01A203, No.2013AA01A213), the NSFC project (Grant No.60933003, No.61202075) , the BNSFproject (Grant No.4133081), and Huawei funding. 2/ Big Data Benchmarking Workshop Publications BigDataBench: a Big Data Benchmark Suite from Web Search Engines. Wanling Gao, et al. The Third Workshop on Architectures and Systems for Big Data (ASBD 2013) in conjunction with ISCA 2013. Characterizing Data Analysis Workloads in Data Centers. Zhen Jia, et al. 2013 IEEE International Symposium on Workload Characterization (IISWC-2013) Characterizing OS behavior of Scale-out Data Center Workloads. Chen Zheng et al. Seventh Annual Workshop on the Interaction amongst Virtualization, Operating Systems and Computer Architecture (WIVOSCA 2013). In Conjunction with ISCA 2013.[ Characterization of Real Workloads of Web Search Engines. Huafeng Xi et al. 2011 IEEE International Symposium on Workload Characterization (IISWC-2011). The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems. Zhen Jia et al. Second workshop of big data benchmarking (WBDB 2012 India) & Lecture Note in Computer -
An Experimental Evaluation of Datacenter Workloads on Low-Power Embedded Micro Servers
An Experimental Evaluation of Datacenter Workloads On Low-Power Embedded Micro Servers Yiran Zhao, Shen Li, Shaohan Hu, Hongwei Wang Shuochao Yao, Huajie Shao, Tarek Abdelzaher Department of Computer Science University of Illinois at Urbana-Champaign fzhao97, shenli3, shu17, hwang172, syao9, hshao5, [email protected] ABSTRACT To reduce datacenter energy cost, power proportional- This paper presents a comprehensive evaluation of an ultra- ity [47] is one major solution studied and pursued by both low power cluster, built upon the Intel Edison based micro academia and industry. Ideally, it allows datacenter power servers. The improved performance and high energy effi- draw to proportionally follow the fluctuating amount of work- ciency of micro servers have driven both academia and in- load, thus saving energy during non-peak hours. However, dustry to explore the possibility of replacing conventional current high-end servers are not energy-proportional and brawny servers with a larger swarm of embedded micro ser- have narrow power spectrum between idling and full uti- vers. Existing attempts mostly focus on mobile-class mi- lization [43], which is far from ideal. Therefore, researchers cro servers, whose capacities are similar to mobile phones. try to improve energy-proportionality using solutions such We, on the other hand, target on sensor-class micro servers, as dynamic provisioning and CPU power scaling. The for- which are originally intended for uses in wearable technolo- mer relies on techniques such as Wake-On-LAN [36] and gies, sensor networks, and Internet-of-Things. Although VM migration [24] to power on/off servers remotely and dy- sensor-class micro servers have much less capacity, they are namically. -
Adaptive Control of Apache Web Server
Adaptive Control of Apache Web Server Erik Reed Abe Ishihara Ole J. Mengshoel Carnegie Mellon University Carnegie Mellon University Carnegie Mellon University Moffett Field, CA 94035 Moffett Field, CA 94035 Moffett Field, CA 94035 [email protected] [email protected] [email protected] Abstract least load. Typical implementations of load balancing deal with round robin balancing for domain name service Traffic to a Web site can vary dramatically. At the same (DNS) [7]. Much of existing frameworks and previous time it is highly desirable that a Web site is reactive. To research deal with inter-server connections and balanc- provide crisp interaction on thin clients, 150 milliseconds ing, with little or no focus on dynamic parameter adjust- has been suggested as an upper bound on response time. ment of individual servers. Unfortunately, the popular Apache Web server is lim- Focusing on an individual Web server rather than a ited in its capabilities to be reactive under varying traf- cluster of machines, we investigate in this paper an alter- fic. To address this problem, we design in this paper an native approach to ensuring quick and predictable user adaptive controller for the Apache Web server. A modi- response, namely feedback control and specifically Min- fied recursive least squares algorithm is used to identify imum Degree Pole Placement control [5]. This can be in system dynamics and a minimum degree pole placement an environment where inter-server load balancing is al- controller is implemented to adjust the maximum num- ready in place, or when a single server is handling Web ber of concurrent connections. -
Energy Efficiency of Server Virtualization
Energy Efficiency of Server Virtualization Jukka Kommeri Tapio Niemi Olli Helin Helsinki Institute of Physics, Helsinki Institute of Physics, Helsinki Institute of Physics, Technology program, CERN, Technology program, CERN, Technology program, CERN, CH-1211 Geneva 23, Switzerland CH-1211Geneva 23, Switzerland CH-1211 Geneva 23, Switzerland [email protected] [email protected] [email protected] Abstract—The need for computing power keeps on growing. combine several services into one physical server. In this The rising energy expenses of data centers have made server way, these technologies make it possible to take better consolidation and virtualization important research areas. advantage of hardware resources. Virtualization and its performance have received a lot of attention and several studies can be found on performance. In this study, we focus on energy efficiency of different So far researches have not studied the overall performance virtualization technologies. Our aim is to help the system and energy efficiency of server consolidation. In this paper we administrator to decide how services should be consolidated study the effect of server consolidation on energy efficiency with to minimize energy consumption without violating quality an emphasis on quality of service. We have studied this with of service agreements. several synthetic benchmarks and with realistic server load by performing a large set of measurements. We found out that We studied energy consumption of virtualized servers with energy-efficiency depends on the load of the virtualized service two open source virtualization solutions; KVM and Xen. and the number of virtualized servers. Idle virtual servers do They were tested both under load and idle. -
Remote Profiling of Resource Constraints of Web Servers Using
Remote Profiling of Resource Constraints of Web Servers Using Mini-Flash Crowds Pratap Ramamurthy Vyas Sekar Aditya Akella Balachander Krishnamurthy UW-Madison CMU UW-Madison AT&T–Labs Research Anees Shaikh IBM Research Abstract often limited, this can be difficult. Hence, large providers Unexpected surges in Web request traffic can exercise who can afford it typically resort to over-provisioning, or server-side resources (e.g., access bandwidth, process- employ techniques such as distributed content delivery or ing, storage etc.) in undesirable ways. Administra- dynamic server provisioning, to minimize the impact of tors today do not have requisite tools to understand the unexpected surges in request traffic. Smaller application impact of such “flash crowds” on their servers. Most providers may trade-off robustness to large variations in Web servers either rely on over-provisioning and admis- workload for a less expensive infrastructure that is provi- sion control, or use potentially expensive solutions like sioned for the expected common case. CDNs, to ensure high availability in the face of flash This approach still leaves operators without a sense crowds. A more fine-grained understanding of the per- of how their application infrastructure will handle large formance of individual server resources under emulated increases in traffic, due to planned events such as annual but realistic and controlled flash crowd-like conditions sales or Web casts, or unexpected flash crowds. While can aid administrators to make more efficient resource these events may not occur frequently, the inability of the management decisions. In this paper, we present mini- infrastructure to maintain reasonably good service, or at flash crowds (MFC) – a light-weight profiling service least degrade gracefully, can lead to significant loss of that reveals resource bottlenecks in a Web server infras- revenue and dissatisfied users. -
Optimal Power Allocation in Server Farms
Optimal Power Allocation in Server Farms Anshul Gandhi∗ Mor Harchol-Balter∗ Rajarshi Das† Charles Lefurgy†† March 2009 CMU-CS-09-113 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 ∗School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA †IBM T.J. Watson Research Center, Hawthorne, NY, USA ††IBM Research, Austin, TX, USA Research supported by NSF SMA/PDOS Grant CCR-0615262 and a 2009 IBM Faculty Award. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of NSF or IBM. Keywords: Power Management, Server Farm, Response Time, Power-to-Frequency, Data Center Abstract Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now looking for solutions on how to best make use of their available power. An important question which arises in this context is how to distribute available power among servers in a server farm so as to get maximum perfor- mance. By giving more power to a server, one can get higher server frequency (speed). Hence it is commonly believed that for a given power budget, performance can be maximized by oper- ating servers at their highest power levels. However, it is also conceivable that one might prefer to run servers at their lowest power levels, which allows more servers for a given power budget. To fully understand the effect of power allocation on performance in a server farm with a fixed power budget, we introduce a queueing theoretic model, which also allows us to predict the opti- mal power allocation in a variety of scenarios. -
Red Hat Enterprise Virtualization Performance: Specvirt™ Benchmark
RED HAT ENTERPRISE VIRTUALIZATION PERFORMANCE: SPECVIRT™ BENCHMARK DATASHEET AT A GLANCE OVERVIEW ® • The performance of Red Hat Red Hat Enterprise Virtualization (RHEV) is the strategic virtualization alternative for organizations Enterprise Virtualization can looking for better total cost of ownership, faster return on investment, accelerated break-even, and be compared to other freedom from vendor lock-in. virtualization platforms RHEV consists of both a hypervisor technology and an enterprise virtualization manager. The RHEV using the SPECvirt_sc2010 hypervisor, based on the Red Hat Enterprise Linux® kernel and the Kernel-based Virtual Machine benchmark. (KVM) hypervisor technology, offers the high performance and scalability that Red Hat is known for. • SPECvirt_sc2010 measures Hypervisor performance is a key factor in deciding which virtualization platform to implement, and hypervisor performance using Red Hat Enterprise Virtualization performance, as measured by the SPECvirt_sc2010® benchmark, realistic workloads. leads the industry both in terms of highest overall performance and highest number of performant As of March 1, 2013, RHEV virtual machines on a single server. leads VMware in performance on comparable WHAT IS SPECVIRT_SC2010®? servers defined by CPU count. SPECvirt_sc2010 is the first vendor-neutral benchmark designed to measure the performance of • The RHEV subscription model datacenter servers that are used for server virtualization. The benchmark was developed by the provides high performance non-profit Standard Performance Evaluation Corporation (SPEC) virtualization subcommittee, at a lower cost-per-SPECvirt_ whose members and contributors include AMD, Dell, Fujitsu, HP, IBM, Intel, Oracle, Red Hat, Unisys sc2010 than VMware. and VMware. SPECvirt_sc2010 uses realistic workloads and SPEC’s proven performance measurement method- ologies to enable vendors, users and researchers to compare system performance across multiple hardware, virtualization platforms, and applications.