Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters
|
|
- Angelina Paul
- 5 years ago
- Views:
Transcription
1 Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters Gregor von Laszewski, Lizhe Wang, Andrew J. Younge, Xi He Service Oriented Cyberinfrastructure Lab Rochester Institute of Technology, Rochester, NY IEEE Cluster 2009
2 Outline Introduction System Model The Scheduling Algorithm Implementation Simulation Conclusion and Future Work 2
3 Introduction (1/2) Today s high performance computers consume tremendous amounts of energy. And every 10 increase of temperature leads to a doubling of the system failure rate. Dynamic voltage and frequency scaling (DVFS) is an efficient technology to control the process power consumption. Processors can be operated in several frequencies with different supply voltage. Intel SpeedStep AMD PowerNow! 3
4 Introduction (2/2) Virtual machine technology is adopted for high end computing to achieve efficient computing resource usage. In this paper, we focus on implementing a power-aware scheduling algorithm where VMs are dynamically provided for executing jobs. This algorithm is to minimize the processor power dissipating by scaling down processor frequencies without drastically increasing the overall VM execution time. 4
5 System Model (1/5) Performance Model (1/2) An Operating Point is defined as: op j = v op, s op We can define a set of j operating points as: OP = 1 j J op j Supply Voltage (V) Frequency (GHz)
6 System Model (2/5) Performance Model (2/2) E = E dynamic + E static P dynamic = ACv 2 s E dynamic = P dynamic Δt E E dynamic E v 2 s Δt t t
7 Cluster Model System Model (3/5) A Process Element is defined as: pe k = op pe, v pe, s pe We can define a cluster C as a collection of k PEs: C = pe k 1 k K 7
8 VM Model A VM is defined as: System Model (4/5) vm i = s r, t, t r We can define a set of i VMs as: VM = vm i 1 i I 8
9 System Model (5/5) Virtual Machine Mapping We need a function f, which maps VM to certain PE that operated in certain operating point: f: vm i pe k. s pe, pe k. v pe, vm i VM, pe k C 9
10 The Scheduling Algorithm (1/6) 10
11 The Scheduling Algorithm (2/6) The rules of algorithm: 1. Minimize the process supply voltage by scaling down the processor frequency. 2. Schedule VMs to PEs with low voltage and try not to scale PE to high voltages. 11
12 The Scheduling Algorithm (3/6) Set all PEs running to the lowest voltage and processor speed, s min. Define pe k. s a as the available processor speed if the processor does not change its operating point. Since no virtual machine are initially scheduled, pe k. s a is initialized with s min. pe k. s is the available PE speed when pe k is operated to a highest level voltage from the current voltage level. pe k. s is initialized with s max. At a predefined interval, reduce power profiles with Algorithm 3 and schedule all incoming VMs with Algorithm 2. Supply Voltage (V) Frequency (GHz)
13 The Scheduling Algorithm (4/6) Sort the incoming virtual machine requests in decreasing order of required processing frequency, vm i. s r so the VMs with higher requirements are scheduled first. Find a pe n with the most available processor speed. If this PE meets the needs of the VM, schedule it on pe n. Continue for all VMs to be scheduled. 13
14 The Scheduling Algorithm (5/6) If vm i cannot be scheduled, take the pe n with the maximum potential speed, we raise the speed the lowest possible level to satisfy the requirements of vm i. Schedule vm i on pe n. 14
15 The Scheduling Algorithm (6/6) During the interval, a VM may finish execution. If it does, try to lower the operating point of pe n to the lowest possible point which meets the requirements of all currently running VMs on pe n. 15
16 Environment Head node Ubuntu 8.10 OpenNebula 1.2 Intel Pentium 4 CPU Compute node Implementation Ubuntu Server 8.10 Xen unstable nbench Benchmark Tool Intel Core i7-920 (Nehalem) Quad-core Processor Frequency: 1.6GHz, 1.86GHz, 2.13GHz, 2.53GHz, 2.66GHz With Hyper-Threading (4C8T) Measure power consumption using a Watts-Up power meter. 16
17 Power Consumption Variations
18 Performance Impact of VMs 18
19 Simulation Simulate a test cluster with 10, 20, 30, 40, and 50 compute nodes Each node was simulated as a Pentium M at 1.4GHz Simulate 100, 200, 300, 400, and 500 virtual machines deployed on the cluster VMs randomly pick frequency requirements in 100Mhz intervals Use the DVFS scheduling algorithm to schedule VMs on nodes 19
20 DVFS-enabled Cluster Scheduling Simulation Results
21 Observation Observation 1: The scheduling algorithm can reduce power consumption in a DVFS-enabled cluster. Observation 2: In case that the number of PEs is fixed, the power consumption increases as the number of incoming virtual machines increases. Observation 3: In case that the number of incoming virtual machine is fixed, the power consumption decreases as the number of PEs increases. 21
22 Overall operating point distribution Simulate scheduling 200 VMs on 40 PEs. In round 1, most PEs run at the lowest voltage. Over subsequent rounds, the operating points scale up as the utilization rises. The overall distribution varies widely, with the majority of the time running below the maximum operating frequency. 22
23 Conclusion and Future Work The need to minimize wasted server energy becomes important. The field of Green computing provides a way to prevent unnecessary CO 2 emissions and save large amounts of money on operating costs. The proposed algorithm dynamically scaled the operating frequencies and voltages of the compute nodes in a cluster without degrading the VM performance beyond unacceptable levels. Both experimental and analytical results show our algorithm is possible to reduce power consumption efficiently within a DVFS-enabled cluster environment. Future work includes the analysis and measuring of VM migration costs in a cluster, and the development of a temperature-aware scheduling algorithm for multi-core clusters. 23
Efficient Resource Management for Cloud Computing Environments
Efficient Resource Management for Cloud Computing Environments Andrew J. Younge, Gregor von Laszewski, Lizhe Wang Pervasive Technology Institute Indianan University Bloomington, IN USA Sonia Lopez-Alarcon,
More informationEnergy-centric DVFS Controlling Method for Multi-core Platforms
Energy-centric DVFS Controlling Method for Multi-core Platforms Shin-gyu Kim, Chanho Choi, Hyeonsang Eom, Heon Y. Yeom Seoul National University, Korea MuCoCoS 2012 Salt Lake City, Utah Abstract Goal To
More informationEnergy efficient mapping of virtual machines
GreenDays@Lille Energy efficient mapping of virtual machines Violaine Villebonnet Thursday 28th November 2013 Supervisor : Georges DA COSTA 2 Current approaches for energy savings in cloud Several actions
More informationBest Practices for Setting BIOS Parameters for Performance
White Paper Best Practices for Setting BIOS Parameters for Performance Cisco UCS E5-based M3 Servers May 2013 2014 Cisco and/or its affiliates. All rights reserved. This document is Cisco Public. Page
More informationDesigning Power-Aware Collective Communication Algorithms for InfiniBand Clusters
Designing Power-Aware Collective Communication Algorithms for InfiniBand Clusters Krishna Kandalla, Emilio P. Mancini, Sayantan Sur, and Dhabaleswar. K. Panda Department of Computer Science & Engineering,
More informationPOWER MANAGEMENT AND ENERGY EFFICIENCY
POWER MANAGEMENT AND ENERGY EFFICIENCY * Adopted Power Management for Embedded Systems, Minsoo Ryu 2017 Operating Systems Design Euiseong Seo (euiseong@skku.edu) Need for Power Management Power consumption
More informationPower Consumption of Virtual Machine Live Migration in Clouds. Anusha Karur Manar Alqarni Muhannad Alghamdi
Power Consumption of Virtual Machine Live Migration in Clouds Anusha Karur Manar Alqarni Muhannad Alghamdi Content Introduction Contribution Related Work Background Experiment & Result Conclusion Future
More informationPERFORMANCE CONSTRAINT AND POWER-AWARE ALLOCATION FOR USER REQUESTS IN VIRTUAL COMPUTING LAB
PERFORMANCE CONSTRAINT AND POWER-AWARE ALLOCATION FOR USER REQUESTS IN VIRTUAL COMPUTING LAB Nguyen Quang Hung, Nam Thoai, Nguyen Thanh Son Ho Chi Minh City University of Technology, Vietnam Corresponding
More informationTowards Energy-Efficient Reactive Thermal Management in Instrumented Datacenters
Towards Energy-Efficient Reactive Thermal Management in Instrumented Datacenters Ivan Rodero1, Eun Kyung Lee1, Dario Pompili1, Manish Parashar1, Marc Gamell2, Renato J. Figueiredo3 1 NSF Center for Autonomic
More informationA Study on Optimally Co-scheduling Jobs of Different Lengths on CMP
A Study on Optimally Co-scheduling Jobs of Different Lengths on CMP Kai Tian Kai Tian, Yunlian Jiang and Xipeng Shen Computer Science Department, College of William and Mary, Virginia, USA 5/18/2009 Cache
More informationEfficient Evaluation and Management of Temperature and Reliability for Multiprocessor Systems
Efficient Evaluation and Management of Temperature and Reliability for Multiprocessor Systems Ayse K. Coskun Electrical and Computer Engineering Department Boston University http://people.bu.edu/acoskun
More informationDeveloping a Powerful yet Inexpensive Computational Infrastructure for the UT Dept. of Nuclear Engineering. David D. Dixon April 8, 2009
Developing a Powerful yet Inexpensive Computational Infrastructure for the UT Dept. of Nuclear Engineering David D. Dixon April 8, 2009 Overview Status of Existing Computational Infrastructure General
More informationCHAPTER 6 ENERGY AWARE SCHEDULING ALGORITHMS IN CLOUD ENVIRONMENT
CHAPTER 6 ENERGY AWARE SCHEDULING ALGORITHMS IN CLOUD ENVIRONMENT This chapter discusses software based scheduling and testing. DVFS (Dynamic Voltage and Frequency Scaling) [42] based experiments have
More informationLEoNIDS: a Low-latency and Energyefficient Intrusion Detection System
LEoNIDS: a Low-latency and Energyefficient Intrusion Detection System Nikos Tsikoudis Thesis Supervisor: Evangelos Markatos June 2013 Heraklion, Greece Low-Power Design Low-power systems receive significant
More informationA Fine-grained Performance-based Decision Model for Virtualization Application Solution
A Fine-grained Performance-based Decision Model for Virtualization Application Solution Jianhai Chen College of Computer Science Zhejiang University Hangzhou City, Zhejiang Province, China 2011/08/29 Outline
More informationAn Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform
An Experimental Study of Load Balancing of OpenNebula Open-Source Cloud Computing Platform A B M Moniruzzaman, StudentMember, IEEE Kawser Wazed Nafi Syed Akther Hossain, Member, IEEE & ACM Abstract Cloud
More informationCHAPTER 6 STATISTICAL MODELING OF REAL WORLD CLOUD ENVIRONMENT FOR RELIABILITY AND ITS EFFECT ON ENERGY AND PERFORMANCE
143 CHAPTER 6 STATISTICAL MODELING OF REAL WORLD CLOUD ENVIRONMENT FOR RELIABILITY AND ITS EFFECT ON ENERGY AND PERFORMANCE 6.1 INTRODUCTION This chapter mainly focuses on how to handle the inherent unreliability
More informationECE 172 Digital Systems. Chapter 15 Turbo Boost Technology. Herbert G. Mayer, PSU Status 8/13/2018
ECE 172 Digital Systems Chapter 15 Turbo Boost Technology Herbert G. Mayer, PSU Status 8/13/2018 1 Syllabus l Introduction l Speedup Parameters l Definitions l Turbo Boost l Turbo Boost, Actual Performance
More informationExperiences with the Sparse Matrix-Vector Multiplication on a Many-core Processor
Experiences with the Sparse Matrix-Vector Multiplication on a Many-core Processor Juan C. Pichel Centro de Investigación en Tecnoloxías da Información (CITIUS) Universidade de Santiago de Compostela, Spain
More informationIntroduction to Energy-Efficient Software 2 nd life talk
Introduction to Energy-Efficient Software 2 nd life talk Intel Software and Solutions Group Bob Steigerwald Nov 8, 2007 Taylor Kidd Nov 15, 2007 Agenda Demand for Mobile Computing Devices What is Energy-Efficient
More informationCPU Clock Ratio, CPU Frequency The settings above are synchronous to those under the same items on the Advanced Frequency Settings menu.
Advanced CPU Core Features CPU Clock Ratio, CPU Frequency The settings above are synchronous to those under the same items on the Advanced Frequency Settings menu. CPU PLL Selection Allows you to set the
More informationarxiv: v1 [cs.ne] 19 Feb 2013
A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud Nguyen Quang-Hung 1, Pham Dac Nien 2, Nguyen Hoai Nam 2, Nguyen Huynh Tuong 1, Nam Thoai 1 arxiv:1302.4519v1 [cs.ne] 19 Feb
More informationEnergy Models for DVFS Processors
Energy Models for DVFS Processors Thomas Rauber 1 Gudula Rünger 2 Michael Schwind 2 Haibin Xu 2 Simon Melzner 1 1) Universität Bayreuth 2) TU Chemnitz 9th Scheduling for Large Scale Systems Workshop July
More informationCompetitive Power Savings with VMware Consolidation on the Dell PowerEdge 2950
Competitive Power Savings with VMware Consolidation on the Dell PowerEdge 2950 By Scott Hanson Dell Enterprise Technology Center Dell Enterprise Technology Center www.delltechcenter.com August 2007 Contents
More informationDEMYSTIFYING INTEL IVY BRIDGE MICROARCHITECTURE
DEMYSTIFYING INTEL IVY BRIDGE MICROARCHITECTURE Roger Luis Uy College of Computer Studies, De La Salle University Abstract: Tick-Tock is a model introduced by Intel Corporation in 2006 to show the improvement
More informationR-Storm: A Resource-Aware Scheduler for STORM. Mohammad Hosseini Boyang Peng Zhihao Hong Reza Farivar Roy Campbell
R-Storm: A Resource-Aware Scheduler for STORM Mohammad Hosseini Boyang Peng Zhihao Hong Reza Farivar Roy Campbell Introduction STORM is an open source distributed real-time data stream processing system
More informationQuantifying power consumption variations of HPC systems using SPEC MPI benchmarks
Center for Information Services and High Performance Computing (ZIH) Quantifying power consumption variations of HPC systems using SPEC MPI benchmarks EnA-HPC, Sept 16 th 2010, Robert Schöne, Daniel Molka,
More informationA Cool Scheduler for Multi-Core Systems Exploiting Program Phases
IEEE TRANSACTIONS ON COMPUTERS, VOL. 63, NO. 5, MAY 2014 1061 A Cool Scheduler for Multi-Core Systems Exploiting Program Phases Zhiming Zhang and J. Morris Chang, Senior Member, IEEE Abstract Rapid growth
More informationA Simple Model for Estimating Power Consumption of a Multicore Server System
, pp.153-160 http://dx.doi.org/10.14257/ijmue.2014.9.2.15 A Simple Model for Estimating Power Consumption of a Multicore Server System Minjoong Kim, Yoondeok Ju, Jinseok Chae and Moonju Park School of
More informationInternational Journal of Science, Engineering and Management (IJSEM) Vol 2, Issue 9, September 2017 Green Enhancement in Cloud Computing Environments
Green Enhancement in Cloud Computing Environments [1] Dr.Vimaladevi K, [2] Princy Rufina C, [3] Vigneesh C.S [1] Assistant Professor, 2PG Scholar, 3PG Scholar, Velammal College of Engineering, Anna University
More informationfor Power Energy and
Engineered for Power Management: Dell PowerEdge Servers Are Designed to Help Save Energy and Reduce Costs ABSTRACT Keeping up with the rising cost of energy is one of the greatest challenges facing IT
More informationEvaluating CPU utilization in a Cloud Environment
Evaluating CPU utilization in a Cloud Environment Presenter MSCS, KFUPM Thesis Committee Members Dr. Farag Azzedin (Advisor) Dr. Mahmood Khan Naizi Dr. Salahdin Adam ICS Department, KFUPM 6/9/2017 2 of
More informationA task migration algorithm for power management on heterogeneous multicore Manman Peng1, a, Wen Luo1, b
5th International Conference on Advanced Materials and Computer Science (ICAMCS 2016) A task migration algorithm for power management on heterogeneous multicore Manman Peng1, a, Wen Luo1, b 1 School of
More informationCHAPTER 7 IMPLEMENTATION OF DYNAMIC VOLTAGE SCALING IN LINUX SCHEDULER
73 CHAPTER 7 IMPLEMENTATION OF DYNAMIC VOLTAGE SCALING IN LINUX SCHEDULER 7.1 INTRODUCTION The proposed DVS algorithm is implemented on DELL INSPIRON 6000 model laptop, which has Intel Pentium Mobile Processor
More informationThe Performance Improvement of an Enhanced CPU Scheduler Using Improved D_EDF Scheduling Algorithm
, pp.287-296 http://dx.doi.org/10.14257/ijhit.2013.6.5.27 The Performance Improvement of an Enhanced CPU Scheduler Using Improved D_EDF Scheduling Algorithm Chia-Ying Tseng 1 and Po-Chun Huang 2 Department
More informationPower-Aware Compile Technology. Xiaoming Li
Power-Aware Compile Technology Xiaoming Li Frying Eggs Future CPU? Watts/cm 2 1000 100 10 1 i386 Hot plate i486 Nuclear Reactor Pentium III processor Pentium II processor Pentium Pro processor Pentium
More informationPower Control in Virtualized Data Centers
Power Control in Virtualized Data Centers Jie Liu Microsoft Research liuj@microsoft.com Joint work with Aman Kansal and Suman Nath (MSR) Interns: Arka Bhattacharya, Harold Lim, Sriram Govindan, Alan Raytman
More informationPhase-Based Application-Driven Power Management on the Single-chip Cloud Computer
Phase-Based Application-Driven Power Management on the Single-chip Cloud Computer Nikolas Ioannou, Michael Kauschke, Matthias Gries, and Marcelo Cintra University of Edinburgh Intel Labs Braunschweig Introduction
More informationExperimental Calibration and Validation of a Speed Scaling Simulator
IEEE MASCOTS 2016 Experimental Calibration and Validation of a Speed Scaling Simulator Arsham Skrenes Carey Williamson Department of Computer Science University of Calgary Speed Scaling: Inherent Tradeoffs
More informationIBM InfoSphere Streams v4.0 Performance Best Practices
Henry May IBM InfoSphere Streams v4.0 Performance Best Practices Abstract Streams v4.0 introduces powerful high availability features. Leveraging these requires careful consideration of performance related
More informationA Probabilistic Graphical Model-based Approach for Minimizing Energy under Performance Constraints
A Probabilistic Graphical Model-based Approach for Minimizing Energy under Performance Constraints Nikita Mishra, Huazhe Zhang, John Lafferty and Hank Hoffmann University of Chicago Fraction of time CPU
More informationResource-Conscious Scheduling for Energy Efficiency on Multicore Processors
Resource-Conscious Scheduling for Energy Efficiency on Andreas Merkel, Jan Stoess, Frank Bellosa System Architecture Group KIT The cooperation of Forschungszentrum Karlsruhe GmbH and Universität Karlsruhe
More informationIMPROVING ENERGY EFFICIENCY THROUGH PARALLELIZATION AND VECTORIZATION ON INTEL R CORE TM
IMPROVING ENERGY EFFICIENCY THROUGH PARALLELIZATION AND VECTORIZATION ON INTEL R CORE TM I5 AND I7 PROCESSORS Juan M. Cebrián 1 Lasse Natvig 1 Jan Christian Meyer 2 1 Depart. of Computer and Information
More informationManaging Hardware Power Saving Modes for High Performance Computing
Managing Hardware Power Saving Modes for High Performance Computing Second International Green Computing Conference 2011, Orlando Timo Minartz, Michael Knobloch, Thomas Ludwig, Bernd Mohr timo.minartz@informatik.uni-hamburg.de
More informationA2E: Adaptively Aggressive Energy Efficient DVFS Scheduling for Data Intensive Applications
A2E: Adaptively Aggressive Energy Efficient DVFS Scheduling for Data Intensive Applications Li Tan 1, Zizhong Chen 1, Ziliang Zong 2, Rong Ge 3, and Dong Li 4 1 University of California, Riverside 2 Texas
More informationA+ Guide to Managing & Maintaining Your PC, 8th Edition. Chapter 5 Supporting Processors and Upgrading Memory
Chapter 5 Supporting Processors and Upgrading Memory Objectives Learn about the characteristics and purposes of Intel and AMD processors used for personal computers Learn how to install and upgrade a processor
More informationNow we are going to speak about the CPU, the Central Processing Unit.
Now we are going to speak about the CPU, the Central Processing Unit. The central processing unit or CPU is the component that executes the instructions of the program that is stored in the computer s
More informationNetworks and/in data centers! Dr. Paola Grosso! System and Network Engineering (SNE) research group! UvA!
Networks and/in data centers Dr. Paola Grosso System and Network Engineering (SNE) research group UvA Email: p.grosso@uva.nl ICT for sustainability Green by ICT or Green ICT. We ll cover in my presentation:
More informationQuad-core Press Briefing First Quarter Update
Quad-core Press Briefing First Quarter Update AMD Worldwide Server/Workstation Marketing C O N F I D E N T I A L Outstanding Dual-core Performance Toady Average of scores places AMD ahead by 2% Average
More informationAMD Opteron Processors In the Cloud
AMD Opteron Processors In the Cloud Pat Patla Vice President Product Marketing AMD DID YOU KNOW? By 2020, every byte of data will pass through the cloud *Source IDC 2 AMD Opteron In The Cloud October,
More informationCPU Benchmarks Over 1,000,000 CPUs Benchmarked
1 of 5 10/16/2018, 9:04 AM Home Software Hardware Benchmarks Services Store Support Forums About Us Home» CPU Benchmarks» New Desktop CPU Performance CPU Benchmarks Video Card Benchmarks Hard Drive Benchmarks
More informationVMware vcenter. Update Manager 5.0 Performance and Best Practices. Performance Study TECHNICAL WHITE PAPER
VMware vcenter Update Manager 5.0 Performance and Best Practices Performance Study TECHNICAL WHITE PAPER Table of Contents Introduction... 3 Benchmarking Methodology... 3 Experimental Setup... 3 vsphere
More informationCFS-v: I/O Demand-driven VM Scheduler in KVM
CFS-v: Demand-driven VM Scheduler in KVM Hyotaek Shim and Sung-Min Lee (hyotaek.shim, sung.min.lee@samsung.com) Software R&D Center, Samsung Electronics 2014. 10. 16 Problem in Server Consolidation 2/16
More informationEnergy Conservation In Computational Grids
Energy Conservation In Computational Grids Monika Yadav 1 and Sudheer Katta 2 and M. R. Bhujade 3 1 Department of Computer Science and Engineering, IIT Bombay monika@cse.iitb.ac.in 2 Department of Electrical
More informationPower Management for Embedded Systems
Power Management for Embedded Systems Minsoo Ryu Hanyang University Why Power Management? Battery-operated devices Smartphones, digital cameras, and laptops use batteries Power savings and battery run
More informationNetwork Design Considerations for Grid Computing
Network Design Considerations for Grid Computing Engineering Systems How Bandwidth, Latency, and Packet Size Impact Grid Job Performance by Erik Burrows, Engineering Systems Analyst, Principal, Broadcom
More informationThe Power Wall. Why Aren t Modern CPUs Faster? What Happened in the Late 1990 s?
The Power Wall Why Aren t Modern CPUs Faster? What Happened in the Late 1990 s? Edward L. Bosworth, Ph.D. Associate Professor TSYS School of Computer Science Columbus State University Columbus, Georgia
More informationMediaTek CorePilot. Heterogeneous Multi-Processing Technology. Delivering extreme compute performance with maximum power efficiency
MediaTek CorePilot Heterogeneous Multi-Processing Technology Delivering extreme compute performance with maximum power efficiency In July 2013, MediaTek delivered the industry s first mobile system on
More informationLEEN: Locality/Fairness- Aware Key Partitioning for MapReduce in the Cloud
LEEN: Locality/Fairness- Aware Key Partitioning for MapReduce in the Cloud Shadi Ibrahim, Hai Jin, Lu Lu, Song Wu, Bingsheng He*, Qi Li # Huazhong University of Science and Technology *Nanyang Technological
More informationA+ Guide to Hardware: Managing, Maintaining, and Troubleshooting, 5e. Chapter 4 Supporting Processors
A+ Guide to Hardware: Managing, Maintaining, and Troubleshooting, 5e Chapter 4 Supporting Processors Objectives Learn about the characteristics and purposes of Intel and AMD processors used for personal
More informationEfficient Power Management
Efficient Power Management on Dell PowerEdge Servers with AMD Opteron Processors Efficient power management enables enterprises to help reduce overall IT costs by avoiding unnecessary energy use. This
More informationA Survey on Green Computing Techniques
A Survey on Green Computing Techniques Sonu Choudhary Department of Computer Science, Acropolis Institute of Technology and Research Indore bypass road Mangliya square Abstract Today computational power
More informationManaging Data Center Power and Cooling
Managing Data Center Power and Cooling with AMD Opteron Processors and AMD PowerNow! Technology Avoiding unnecessary energy use in enterprise data centers can be critical for success. This article discusses
More informationTHERMAL BENCHMARK AND POWER BENCHMARK SOFTWARE
Nice, Côte d Azur, France, 27-29 September 26 THERMAL BENCHMARK AND POWER BENCHMARK SOFTWARE Marius Marcu, Mircea Vladutiu, Horatiu Moldovan and Mircea Popa Department of Computer Science, Politehnica
More informationA TAXONOMY AND SURVEY OF ENERGY-EFFICIENT DATA CENTERS AND CLOUD COMPUTING SYSTEMS
A TAXONOMY AND SURVEY OF ENERGY-EFFICIENT DATA CENTERS AND CLOUD COMPUTING SYSTEMS Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya Prepared by: Dr. Faramarz Safi Islamic Azad University,
More informationPower Measurements using performance counters CSL862: Low-Power Computing By Radhika D (2014SIY7530)
Power Measurements using performance counters CSL862: Low-Power Computing By Radhika D (214SIY753) 1 Objective: To observe and note the performance and power consumption of Raspberry PI for various benchmark
More informationArachne. Core Aware Thread Management Henry Qin Jacqueline Speiser John Ousterhout
Arachne Core Aware Thread Management Henry Qin Jacqueline Speiser John Ousterhout Granular Computing Platform Zaharia Winstein Levis Applications Kozyrakis Cluster Scheduling Ousterhout Low-Latency RPC
More informationLinux Kernel Hacking Free Course
Linux Kernel Hacking Free Course 3 rd edition G.Grilli, University of me Tor Vergata IRQ DISTRIBUTION IN MULTIPROCESSOR SYSTEMS April 05, 2006 IRQ distribution in multiprocessor systems 1 Contents: What
More informationQoS Handling with DVFS (CPUfreq & Devfreq)
QoS Handling with DVFS (CPUfreq & Devfreq) MyungJoo Ham SW Center, 1 Performance Issues of DVFS Performance Sucks w/ DVFS! Battery-life Still Matters More Devices (components) w/ DVFS More Performance
More informationCOL862 - Low Power Computing
COL862 - Low Power Computing Power Measurements using performance counters and studying the low power computing techniques in IoT development board (PSoC 4 BLE Pioneer Kit) and Arduino Mega 2560 Submitted
More informationarxiv: v1 [cs.dc] 28 Oct 2014
Chapter 1 Energy-Aware Lease Scheduling in Virtualized Data Centers Nguyen Quang-Hung, Nam Thoai, Nguyen Thanh Son, Duy-Khanh Le arxiv:1410.7815v1 [cs.dc] 28 Oct 2014 Abstract Energy efficiency has become
More informationAn Efficient Virtual CPU Scheduling Algorithm for Xen Hypervisor in Virtualized Environment
An Efficient Virtual CPU Scheduling Algorithm for Xen Hypervisor in Virtualized Environment Chia-Ying Tseng 1 and Po-Chun Huang 2 Department of Computer Science and Engineering, Tatung University #40,
More informationEvaluating the Impact of Virtualization on Performance and Power Dissipation
Evaluating the Impact of Virtualization on Performance and Power Dissipation Francisco J. Clemente-Castelló, Sonia Cervera, Rafael Mayo and Enrique S. Quintana-Ortí Department of Engineering and Computer
More informationMICROPROCESSOR ARCHITECTURE
MICROPROCESSOR ARCHITECTURE UOP S.E.COMP (SEM-I) MULTICORE DESIGN Prof.P.C.Patil Department of Computer Engg Matoshri College of Engg.Nasik pcpatil18@gmail.com. History 2 History The most important part
More informationImproving CPU Performance of Xen Hypervisor in Virtualized Environment
ISSN: 2393-8528 Contents lists available at www.ijicse.in International Journal of Innovative Computer Science & Engineering Volume 5 Issue 3; May-June 2018; Page No. 14-19 Improving CPU Performance of
More informationDVFS Space Exploration in Power-Constrained Processing-in-Memory Systems
DVFS Space Exploration in Power-Constrained Processing-in-Memory Systems Marko Scrbak and Krishna M. Kavi Computer Systems Research Laboratory Department of Computer Science & Engineering University of
More informationOVERHEADS ENHANCEMENT IN MUTIPLE PROCESSING SYSTEMS BY ANURAG REDDY GANKAT KARTHIK REDDY AKKATI
CMPE 655- MULTIPLE PROCESSOR SYSTEMS OVERHEADS ENHANCEMENT IN MUTIPLE PROCESSING SYSTEMS BY ANURAG REDDY GANKAT KARTHIK REDDY AKKATI What is MULTI PROCESSING?? Multiprocessing is the coordinated processing
More informationCross-layer Optimization for Virtual Machine Resource Management
Cross-layer Optimization for Virtual Machine Resource Management Ming Zhao, Arizona State University Lixi Wang, Amazon Yun Lv, Beihang Universituy Jing Xu, Google http://visa.lab.asu.edu Virtualized Infrastructures,
More informationHardware-Efficient Parallelized Optimization with COMSOL Multiphysics and MATLAB
Hardware-Efficient Parallelized Optimization with COMSOL Multiphysics and MATLAB Frommelt Thomas* and Gutser Raphael SGL Carbon GmbH *Corresponding author: Werner-von-Siemens Straße 18, 86405 Meitingen,
More informationIs Intel s Hyper-Threading Technology Worth the Extra Money to the Average User?
Is Intel s Hyper-Threading Technology Worth the Extra Money to the Average User? Andrew Murray Villanova University 800 Lancaster Avenue, Villanova, PA, 19085 United States of America ABSTRACT In the mid-1990
More informationRT- Xen: Real- Time Virtualiza2on. Chenyang Lu Cyber- Physical Systems Laboratory Department of Computer Science and Engineering
RT- Xen: Real- Time Virtualiza2on Chenyang Lu Cyber- Physical Systems Laboratory Department of Computer Science and Engineering Embedded Systems Ø Consolidate 100 ECUs à ~10 multicore processors. Ø Integrate
More informationQuantifying Load Imbalance on Virtualized Enterprise Servers
Quantifying Load Imbalance on Virtualized Enterprise Servers Emmanuel Arzuaga and David Kaeli Department of Electrical and Computer Engineering Northeastern University Boston MA 1 Traditional Data Centers
More informationPOWER MANAGEMENT IN THE CLUSTER SYSTEM
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Computer Science and Engineering: Theses, Dissertations, and Student Research Computer Science and Engineering, Department
More informationDell PowerEdge R910 SQL OLTP Virtualization Study Measuring Performance and Power Improvements of New Intel Xeon E7 Processors and Low-Voltage Memory
Dell PowerEdge R910 SQL OLTP Virtualization Study Measuring Performance and Power Improvements of New Intel Xeon E7 Processors and Low-Voltage Memory A Dell Technical White Paper Dell, Inc. Waseem Raja
More informationPower Management in Intel Architecture Servers
Power Management in Intel Architecture Servers White Paper Intel Architecture Servers During the last decade, Intel has added several new technologies that enable users to improve the power efficiency
More informationDEDICATED SERVERS WITH EBS
DEDICATED WITH EBS TABLE OF CONTENTS WHY CHOOSE A DEDICATED SERVER? 3 DEDICATED WITH EBS 4 INTEL ATOM DEDICATED 5 AMD OPTERON DEDICATED 6 INTEL XEON DEDICATED 7 MANAGED SERVICES 8 SERVICE GUARANTEES 9
More informationAn Asymmetry-aware Energy-efficient Hypervisor Scheduling Policy for Asymmetric Multi-core
TR-IIS-15-003 An Asymmetry-aware Energy-efficient Hypervisor Scheduling Policy for Asymmetric Multi-core Ching-Chi Lin, You-Cheng Syu, Yi-Chung Chen, Jan-Jan Wu, Pangfeng Liu, Po-Wen Cheng, and Wei-Te
More informationScaling through more cores
Scaling through more cores From single to multi core by Thomas Walther Seminar on 30.11.2015 1/32 Index 1. Introduction 2. Scaling with single core until 2005 Problems and barriers 3. Solution through
More informationHP-DAEMON: High Performance Distributed Adaptive Energy-efficient Matrix-multiplicatiON
HP-DAEMON: High Performance Distributed Adaptive Energy-efficient Matrix-multiplicatiON Li Tan 1, Longxiang Chen 1, Zizhong Chen 1, Ziliang Zong 2, Rong Ge 3, and Dong Li 4 1 University of California,
More informationEnergy Efficient in Cloud Computing
Energy Efficient in Cloud Computing Christoph Aschberger Franziska Halbrainer May 24, 2013 1 of 25 Introduction Energy consumption by Google 2011: 2,675,898 MWh. We found that we use roughly as much electricity
More informationBackground Testing Methodology...4. HD Video Playback Applications Summary Recommendations Conclusion References...
White Paper Intel Software & Services Group Tareq H. Darwish Rajshree Chabukswar Intel Hardware Accelerated High Definition Video Playback Power Analysis Recent media technologies like Blu-ray* have driven
More informationDE0 Nano SoC - CPU Performance and Power
DE0 Nano SoC DE0 Nano SoC - CPU Performance and Power While Running Debian 19 th March 2017 - Satyen Akolkar Group 5 - AR Internet of Things By: Satyen Akolkar OVERVIEW The benchmark was performed by using
More informationIncreasing Cloud Power Efficiency through Consolidation Techniques
Increasing Cloud Power Efficiency through Consolidation Techniques Antonio Corradi, Mario Fanelli, Luca Foschini Dipartimento di Elettronica, Informatica e Sistemistica (DEIS) University of Bologna, Italy
More informationAbhishek Pandey Aman Chadha Aditya Prakash
Abhishek Pandey Aman Chadha Aditya Prakash System: Building Blocks Motivation: Problem: Determining when to scale down the frequency at runtime is an intricate task. Proposed Solution: Use Machine learning
More informationCIS : Scalable Data Analysis
CIS 602-01: Scalable Data Analysis Cloud Workloads Dr. David Koop Scaling Up PC [Haeberlen and Ives, 2015] 2 Scaling Up PC Server [Haeberlen and Ives, 2015] 2 Scaling Up PC Server Cluster [Haeberlen and
More informationTrend Micro Incorporated reserves the right to make changes to this document and to the products described herein without notice.
Trend Micro Incorporated reserves the right to make changes to this document and to the products described herein without notice. Before installing and using the software, please review the readme files,
More informationMinimizing Thermal Variation in Heterogeneous HPC System with FPGA Nodes
Minimizing Thermal Variation in Heterogeneous HPC System with FPGA Nodes Yingyi Luo, Xiaoyang Wang, Seda Ogrenci-Memik, Gokhan Memik, Kazutomo Yoshii, Pete Beckman @ICCD 2018 Motivation FPGAs in data centers
More informationEnergy-efficient Custom Topology-based Dynamic Voltage-frequency Island-enabled Network-on-chip Design
JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE, VOL.18, NO.3, JUNE, 2018 ISSN(Print) 1598-1657 https://doi.org/10.5573/jsts.2018.18.3.352 ISSN(Online) 2233-4866 Energy-efficient Custom Topology-based
More informationAccurate emulation of CPU performance
Accurate emulation of CPU performance Tomasz Buchert 1 Lucas Nussbaum 2 Jens Gustedt 1 1 INRIA Nancy Grand Est 2 LORIA / Nancy - Université Validation of distributed systems Approaches: Theoretical approach
More informationBig.LITTLE Processing with ARM Cortex -A15 & Cortex-A7
Big.LITTLE Processing with ARM Cortex -A15 & Cortex-A7 Improving Energy Efficiency in High-Performance Mobile Platforms Peter Greenhalgh, ARM September 2011 This paper presents the rationale and design
More information