CSE 591: Energy-Efficient Computing Lecture 4 SLEEP: full-system Anshul Gandhi 347, CS building

Slides:



Advertisements
Similar presentations
VARUN GUPTA Carnegie Mellon University 1 Partly based on joint work with: Anshul Gandhi Mor Harchol-Balter Mike Kozuch (CMU) (CMU) (Intel Research)
Advertisements

CS 142 Lecture Notes: FormsSlide 1 Simple Form Product: Price:
CS 142 Lecture Notes: FormsSlide 1 Simple Form Product: Price:
CSE 691: Energy-Efficient Computing Lecture 20 review Anshul Gandhi 1307, CS building
1 MemScale: Active Low-Power Modes for Main Memory Qingyuan Deng, David Meisner*, Luiz Ramos, Thomas F. Wenisch*, and Ricardo Bianchini Rutgers University.
Power Aware Virtual Machine Placement Yefu Wang. 2 ECE Introduction Data centers are underutilized – Prepared for extreme workloads – Commonly.
CSE 691: Energy-Efficient Computing Lecture 4 SCALING: stateless vs. stateful Anshul Gandhi 1307, CS building
Green Cloud Computing Hadi Salimi Distributed Systems Lab, School of Computer Engineering, Iran University of Science and Technology,
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Simple queuing models (Sec )
Energy Efficient Web Server Cluster Andrew Krioukov, Sara Alspaugh, Laura Keys, David Culler, Randy Katz.
Present By : Bahar Fatholapour M.Sc. Student in Information Technology Mazandaran University of Science and Technology Supervisor:
Cross Cluster Migration Remote access support Adianto Wibisono supervised by : Dr. Dick van Albada Kamil Iskra, M. Sc.
CSE 691: Energy-Efficient Computing Lecture 3 SLEEP: full-system Anshul Gandhi 1307, CS building
VIRTUALISATION OF HADOOP CLUSTERS Dr G Sudha Sadasivam Assistant Professor Department of CSE PSGCT.
Efficient Resource Management for Cloud Computing Environments
CSE598C Project: Dynamic virtual server placement Yoojin Hong.
Power Management of Online Data Intensive Services Meisner et al.
WHAT IS PRIVATE CLOUD? Michał Jędrzejczak Główny Architekt Rozwiązań Infrastruktury IT
Virtualization Technology Prof D M Dhamdhere CSE Department IIT Bombay Moving towards Virtualization… Department of Computer Science and Engineering, IIT.
Dependability Models for Designing Disaster Tolerant Cloud Computing Systems.
Green IT and Data Centers Darshan R. Kapadia Gregor von Laszewski 1.
Department of Computer Science Engineering SRM University
Power Issues in On-chip Interconnection Networks Mojtaba Amiri Nov. 5, 2009.
Virtual Machine Course Rofideh Hadighi University of Science and Technology of Mazandaran, 31 Dec 2009.
Lecture 03: Fundamentals of Computer Design - Trends and Performance Kai Bu
Low-Power Wireless Sensor Networks
Cloud Computing Energy efficient cloud computing Keke Chen.
Building the Infrastructure Grid: Architecture, Design & Deployment Logan McLeod – Database Technology Strategist.
CSE 691: Energy-Efficient Computing Lecture 6 SHARING: distributed vs. local Anshul Gandhi 1307, CS building
1 © Copyright 2010 EMC Corporation. All rights reserved.  Consolidation  Create economies of scale through standardization  Reduce IT costs  Deliver.
An Autonomic Framework in Cloud Environment Jiedan Zhu Advisor: Prof. Gagan Agrawal.
CSE 691: Energy-Efficient Computing Lecture 7 SMARTS: custom-made systems Anshul Gandhi 1307, CS building
Power Management Challenges in Virtualization Environments Congfeng Jiang, Jian Wan, Xianghua Xu, Yunfa Li, Xindong You Grid and Service Computing Technology.
Challenges towards Elastic Power Management in Internet Data Center.
High Performance File System Service for Cloud Computing Kenji Kobayashi, Osamu Tatebe University of Tsukuba, JAPAN.
Dynamic Resource Monitoring and Allocation in a virtualized environment.
Power-Aware Scheduling of Virtual Machines in DVFS-enabled Clusters
CSE 691: Energy-Efficient Computing Lecture 1: Intro and Logistics Anshul Gandhi 1307, CS building
Towards Dynamic Green-Sizing for Database Servers Mustafa Korkmaz, Alexey Karyakin, Martin Karsten, Kenneth Salem University of Waterloo.
Embedded System Lab. 김해천 The TURBO Diaries: Application-controlled Frequency Scaling Explained.
VGreen: A System for Energy Efficient Manager in Virtualized Environments G. Dhiman, G Marchetti, T Rosing ISLPED 2009.
Optimizing Power and Energy Lei Fan, Martyn Romanko.
Virtualization A brief introduction Virtualization1.
Optimizing Power and Data Center Resources Jim Sweeney Enterprise Solutions Consultant, GTSI.
CSE 591: Energy-Efficient Computing Lecture 3 SPEED: processor Anshul Gandhi 347, CS building
CSE 591: Energy-Efficient Computing Lecture 1: Intro and Logistics Anshul Gandhi 347, New CS building
CSE 591: Energy-Efficient Computing Lecture 8 SOURCE: renewables Anshul Gandhi 347, CS building
Power Capping Via Forced Idleness ANSHUL GANDHI Carnegie Mellon Univ. 1.
1 EIT 2.2 Is your company missing out on the cost-savings opportunities offered by data center consolidations? Andy Abbas Co-Founder and Vice President.
A Measured Approach to Virtualization Don Mendonsa Lawrence Livermore National Laboratory NLIT 2008 by LLNL-PRES
Power Provisioning for a Warehouse-Size Computer (ISCA 2007) Authors: Xiabo Fan, Wolf-Dietrich Weber, and Luis Andre Barroso Google Presenter: Kirk Pruhs.
Server Consolidation in Clouds through Gossiping Moreno MarzollaOzalp Babaoglu Fabio Panzieri Università di Bologna, Dip. di Scienze dell'Informazione.
CSE 591: Energy-Efficient Computing Lecture 13 SLEEP: sensors
Anshul Gandhi 347, CS building
Anshul Gandhi 347, CS building
Green cloud computing 2 Cs 595 Lecture 15.
CSE 591: Energy-Efficient Computing Lecture 17 SCALING: survey
Are Low Power Server CPUs Worth the Cost?
CSE 591: Energy-Efficient Computing Lecture 20 SPEED: disks
CSE 591: Energy-Efficient Computing Lecture 21 review
CSE 591: Energy-Efficient Computing Lecture 15 SCALING: storage
CSE 591: Energy-Efficient Computing Lecture 10 SLEEP: network
CS 140 Lecture Notes: Virtual Machines
CSE 591: Energy-Efficient Computing Lecture 19 SPEED: memory
CSE 591: Energy-Efficient Computing Lecture 12 SLEEP: memory
CSE 591: Energy-Efficient Computing Lecture 14 SCALING: setup time
CSE 591: Energy-Efficient Computing Lecture 9 SLEEP: processor
CSE 531: Performance Analysis of Systems Lecture 4: DTMC
CSE 591: Energy-Efficient Computing Lecture 18 SPEED: power
CS 140 Lecture Notes: Virtual Machines
Presentation transcript:

CSE 591: Energy-Efficient Computing Lecture 4 SLEEP: full-system Anshul Gandhi 347, CS building

power_nap paper

US data centers:100 billion kWh by 2011 ?? $$ Server utilization:<30% Idle server power:60% of peak Idle periods:~seconds why important?

What does 30% utilization mean?

Utilization data

Existing techniques 1.Consolidation 2.Sleep states 3.Throttling (DVFS)

PowerNap 1.Simple idea (only 2 states) Minimize power draw in sleep Fast transitions 2.Model (power and response time) 3.PowerNap vs DVFS 4.RAILS

(Potential) Implementation

RAILS

agile paper

agile PowerNap was NOT implemented agile took first REAL step towards that Static consolidation vs Dynamic consolidation How to minimize latency penalties of dynamic consolidation? 3 ideas. agile: dynamic virtualization + PowerNap implementation

agile: main problem

agile: low-power states Turbo C0P statesT states C1 C1E C2 C3 S0C6 S1 S2 S3 G0S4 G2S5 G3

agile: power vs. latency

agile: dynamic consolidation 1.Host power-up 2.VM migration 3.Host power-down