Get documents about "PowerPoint 演示文稿
Document Sample
VGREEN: A SYSTEM FOR
ENERGY EFFICIENT
COMPUTING IN VIRTUALIZED
ENVIRONMENTS
Gaurav Dhiman UC San Diego, La Jolla, CA, USA
Giacomo Marchetti UC San Diego, La Jolla, CA, USA
Tajana Rosing UC San Diego, La Jolla, CA, USA
Keywords: Virtualization, Migration, Energy, Workload Characterization
About Publisher
• International Symposium on
Low Power Electronics and
Design 2009
Proceedings of the 14th
ACM/IEEE international
symposium on Low power
electronics and design
• ISLPED10:
• Omni Hotel, Downtown Austin,
Texas USA
• August 18-20, 2010
About Author 1st : Gaurav Dhiman
vGreen: a system for energy efficient computing in virtualized environmentsGaurav Dhiman,
Giacomo Marchetti, Tajana Rosing
August ISLPED '09: Proceedings of the 14th ACM/IEEE international
2009 symposium on Low power electronics and design
PDRAM: a hybrid PRAM and DRAM main memory systemGaurav Dhiman, Raid Ayoub,
Tajana Rosing
July DAC '09: Proceedings of the 46th Annual Design Automation
2009 Conference
System-level power management using online learningGaurav Dhiman, Tajana Šimunic
Rosing
May IEEE Transactions on Computer-Aided Design of Integrated
2009 Circuits and Systems , Volume 28 Issue 5
Dynamic voltage frequency scaling for multi-tasking systems using online learningGaurav
Dhiman, Tajana Simunic Rosing
August ISLPED '07: Proceedings of the 2007 international symposium on
2007 Low power electronics and design
Dynamic power management using machine learningGaurav Dhiman, Tajana Simunic
Rosing
Novemb ICCAD '06: Proceedings of the 2006 IEEE/ACM international
er 2006 conference on Computer-aided design
About Author 2nd : Giacomo Marchetti
vGreen: a system for energy efficient computing in virtualized
environmentsGaurav Dhiman, Giacomo Marchetti, Tajana Rosing
August ISLPED '09: Proceedings of the 14th ACM/IEEE
2009 international symposium on Low power electronics and
design
Publisher: ACM
In this paper, we present vGreen, a multi-tiered software system
for energy efficient computing in virtualized environments. It
comprises of novel hierarchical metrics that capture power and
performance characteristics of virtual and physical machines, ...
Keywords: energy, migration, virtualization, workload
characterization
About Author 3rd : Tajana Simunic Rosing
Utilizing predictors for efficient thermal management in multiprocessor SoCsAyse Kivilcim Coskun, Tajana Šimunic Rosing, Kenny C. Gross
October 2009 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , Volume 28
Issue 10
Temperature- and Cost-Aware Design of 3D Multiprocessor ArchitecturesAyse K. Coskun, Andrew B. Kahng, Tajana Simunic Rosing
August 2009 DSD '09: Proceedings of the 2009 12th Euromicro Conference on Digital System Design, Architectures,
Methods and Tools
Predict and act: dynamic thermal management for multi-core processorsRaid Zuhair Ayoub, Tajana Simunic Rosing
August 2009 ISLPED '09: Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and
design
vGreen: a system for energy efficient computing in virtualized environmentsGaurav Dhiman, Giacomo Marchetti, Tajana Rosing
August 2009 ISLPED '09: Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and
design
PDRAM: a hybrid PRAM and DRAM main memory systemGaurav Dhiman, Raid Ayoub, Tajana Rosing
July 2009 DAC '09: Proceedings of the 46th Annual Design Automation Conference
Adapting task utility in externally triggered energy harvesting wireless sensing systemsJamie Bradley Steck, Tajana Simunic Rosing
June 2009 INSS'09: Proceedings of the 6th international conference on Networked sensing systems
System-level power management using online learningGaurav Dhiman, Tajana Šimunic Rosing
May 2009 IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , Volume 28
Issue 5
Proactive temperature balancing for low cost thermal management in MPSoCsAyse Kivilcim Coskun, Tajana Simunic Rosing, Kenny C. Gross
November 2008 ICCAD '08: Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Static and dynamic temperature-aware scheduling for multiprocessor SoCsAyse Kivilcim Coskun, Tajana Šimunic Rosing, Keith A. Whisnant,
Kenny C. Gross
September 2008 IEEE Transactions on Very Large Scale Integration (VLSI) Systems , Volume 16 Issue 9
Proactive temperature management in MPSoCsAyse Kivilcim Coskun, Tajana Simunic Rosing, Kenny C. Gross
August 2008 ISLPED '08: Proceeding of the 13th international symposium on Low power electronics and design
Temperature management in multiprocessor SoCs using online learningAyse Kivilcim Coskun, Tajana Simunic Rosing, Kenny C.
Gross
June 2008 DAC '08: Proceedings of the 45th annual Design Automation Conference
An analytical model for the upper bound on temperature differences on a chipShervin Sharifi, Tajana Simunic Rosing
May 2008 GLSVLSI '08: Proceedings of the 18th ACM Great Lakes symposium on VLSI
Accurate Temperature Estimation for Efficient Thermal ManagementShervin Sharifi, ChunChen Liu, Tajana Simunic Rosing
March 2008 ISQED '08: Proceedings of the 9th international symposium on Quality Electronic Design
Bibliometrics: Downloads (6 Weeks): n/a, Downloads (12 Months): n/a, Citation Count: 1
Temperature-aware MPSoC scheduling for reducing hot spots and gradientsAyse Kivilcim Coskun, Tajana Simunic Rosing,
Keith A. Whisnant, Kenny C. Gross
January 2008 ASP-DAC '08: Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Dynamic voltage frequency scaling for multi-tasking systems using online learningGaurav Dhiman, Tajana Simunic Rosing
August 2007 ISLPED '07: Proceedings of the 2007 international symposium on Low power electronics and design
Active sensing platform for wireless structural health monitoringD. Musiani, K. Lin, T. Simunic Rosing
April 2007 IPSN '07: Proceedings of the 6th international conference on Information processing in sensor
networks
Temperature aware task scheduling in MPSoCsAyse Kivilcim Coskun, Tajana Simunic Rosing, Keith Whisnant
April 2007 DATE '07: Proceedings of the conference on Design, automation and test in Europe
Power and reliability management of SoCsTajana Simunic Rosing, Kresimir Mihic, Giovanni De Micheli
April 2007 IEEE Transactions on Very Large Scale Integration (VLSI) Systems , Volume 15 Issue 4
Scheduling Data Delivery in Heterogeneous Wireless Sensor NetworksDaeseob Lim, Jaewook Shim, Tajana Simunic Rosing,
Tara Javidi
December 2006 ISM '06: Proceedings of the Eighth IEEE International Symposium on Multimedia
Dynamic power management using machine learningGaurav Dhiman, Tajana Simunic Rosing
November 2006 ICCAD '06: Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
A simulation methodology for reliability analysis in multi-core SoCsAyse K. Coskun, Tajana Simunic Rosing, Yusuf Leblebici,
Giovanni De Micheli
April 2006 GLSVLSI '06: Proceedings of the 16th ACM Great Lakes symposium on VLSI
Energy estimation of peripheral devices in embedded systemsOzgur Celebican, Tajana Simunic Rosing, Vincent J. Mooney,
III
April 2004 GLSVLSI '04: Proceedings of the 14th ACM Great Lakes symposium on VLSI
Reference
• [1] VMware Dynamic Resource Scheduler,
http://www.vmware.com/files/pdf/drs_datasheet.pdf.
• [2] P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, and
A. Warfield. Xen and the art of virtualization. In Proc. SOSP ’03.
• [3] C. Bienia, S. Kumar, J. P. Singh, and K. Li. The PARSEC benchmark suite:
characterization and architectural implications. In Proc. PACT ’08.
• [4] N. Bobroff, A. Kochut, and K. Beaty. Dynamic placement of virtual machines for
managing SLA violations. In Proc. International Symposium on Integrated Network
Management ’07.
• [5] C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, and A. Warfield.
Live migration of virtual machines. In Proc. NSDI’05.
• [6] G. Dhiman and T. Rosing. System-level power management using online learning. IEEE
Transactions on CAD’09.
• [7] F. Hermenier, X. Lorca, J.-M. Menaud, G. Muller, and J. Lawall. Entropy: a
consolidation manager for clusters. In Proc. VEE’09.
• [8] C. Isci, G. Contreras, and M. Martonosi. Live, runtime phase monitoring and
prediction on real systems with application to dynamic power management. In Proc.
MICRO’06.
• [9] R. Knauerhase, P. Brett, B. Hohlt, T. Li, and S. Hahn. Using OS observations to improve
performance in multicore systems. IEEE Micro’08.
Reference
• [10] M. McNett, D. Gupta, A. Vahdat, and G. M. Voelker. Usher: an extensible framework
for managing custers of virtual machines. In Proc. LISA’07.
• [11] A. Merkel and F. Bellosa. Balancing power consumption in multiprocessor systems.
SIGOPS Oper. Syst. Rev. ’06.
• [12] R. Nathuji and K. Schwan. VirtualPower: coordinated power management in
virtualized enterprise systems. In Proc. SOSP ’07.
• [13] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, and D.
Zagorodnov. The Eucalyptus open-source cloud-computing system. In Proceedings of
Cloud Computing and Its Applications’08.
• [14] R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu. No "power"
struggles: coordinated multi-level power management for the data center. In Proc
ASPLOS’08.
• [15] P. Ranganathan, P. Leech, D. Irwin, and J. Chase. Ensemble-level power management
for dense blade servers. In Proc. ISCA ’06.
• [16] A. Snavely and D. M. Tullsen. Symbiotic job scheduling for a simultaneous
mutlithreading processor. In Proc. ASPLOS’00.
• [17] A. Verma, P. Ahuja, and A. Neogi. Power-aware dynamic placement of HPC
applications. In ICS ’08.
• [18] T. Wood, P. J. Shenoy, A. Venkataramani, and M. S. Yousif. Black-box and gray-box
strategies for virtual machine migration. In Proc. NSDI’07.
Cited by
• Addressing shared resource contention in multicore processors via
scheduling
S Zhuravlev, S Blagodurov, A Fedorova - ACM SIGPLAN Notices, 2010 -
portal.acm.org
• Gentlecool: Cooling aware proactive workload scheduling in multi-machine
systems
R Ayoub, S Sharifi, TS Rosing - Proceedings of DATE 2010 - seelab.ucsd.edu
• PowerTracer: Tracing requests in multi-tier services to save cluster power
consumption
L Yuan, J Zhan, B Sang, L Wang, H Wang - Arxiv preprint arXiv: …, 2010 -
arxiv.org
• A system for online power prediction in virtualized environments using
Gaussian mixture models
G Dhiman, K Mihic, T Rosing - Proceedings of the 47th Design …, 2010 -
portal.acm.org
• Resource-conscious scheduling for energy efficiency on multicore processors
A Merkel, J Stoess, F Bellosa - … of the 5th European conference on …,
2010 - portal.acm.org
Motivation
• Power consumption is a critical design parameter in
modern data center and enterprise environments
• Modern data centers use virtualization to get better fault
and performance isolation, improved system
manageability and reduced infrastructure cost through
resource consolidation and live migration
• However, as we show later on, based on the utilization
levels and characteristics of these different co-located
VMs, the overall power consumption and performance of
the VMs can vary a lot.
Introduction
• vGreen, a multi-tiered software system to manage VM
scheduling across different PMs with the objective of
managing the overall energy efficiency and performance.
• to understand and exploit the relationship between the
architectural characteristics of a VM and its
performance and power consumption.
• We implemented vGreen on a testbed of servers running
Xen as the virtualization software.
• We show that vGreen is able to dynamically characterize
and accordingly schedule the VMs across the PM cluster,
improving the overall performance and energy efficiency
by 20% and 15% respectively compared to state of the
art VM scheduling policies.
Related Works
1. Eucalyptus[13] and Usher[10], open source systems, managing VM
creation and allocation across a PM cluster. Have no VM scheduling
policies to dynamically consolidate or redistribute VMs.
2. In [18], a VM scheduling system, which dynamically schedules the
VMs across the PMs based on their CPU, memory and network
utilization. Focusing on better overall performance.
3. The Distributed resource scheduler (DRS) from VMware [1], a
proprietary solution, also uses VM scheduling to perform automated
load balancing in response to CPU and memory pressure.
4. in [4], the authors propose VM scheduling algorithms for dynamic
consolidation and redistribution of VMs for managing performance and
SLA violations.
5. in [7] propose Entropy, which uses constraint programming to
determine a globally optimal solution for VM scheduling in contrast to
the first fit decreasing heuristic used by [18, 4], which can result in
globally sub-optimal placement of VMs.
• none of these VM scheduling algorithms take into account the impact
of the policy decisions on the energy consumption in the system.
Related Works
1. In [12], the authors propose VirtualPower, uses the power
management decisions of the guest OS on virtual power states as
hints to run local and global policies across the PMs. It relies on
efficient power management policies in the guest OS(in which sensors
are virtualized), and does no VM characterization at the hypervisor
level.
2. In [14], a co-ordinated multi-level solution for power management in
data centers is proposed. The model uses power estimation (using
power sensors) and workload utilization levels to drive VM placement
and power management. However, the model and results are based
on offline trace driven analysis and simulations.
• both [12] and [14] do not consider VM’s architectural characteristics
• In [17], the authors use VM characteristics like cache footprint and
working set to drive power aware placement of VMs. But their study
assumes an HPC application environment, where the VM
characteristics are known in advance. Besides, their evaluation is
based on simulations.
Primary contributions
1. a system for characterization of VMs in a virtualized
environment based on novel hierarchical metrics that
capture power and performance profiles of the VMs
2. online VM characterization to drive dynamic VM
scheduling across a PM cluster for overall
performance and energy efficiency for general
purpose workloads.
3. implement the proposed system on a real life testbed
and through extensive experiments and measurements
highlight the benefits of the approach over existing
state of the art VM scheduling systems
vGreen Design: Motivation
• To understand the co-
relation between VM
Intel Intel
characteristics and these Quad Quad
metrics we performed some Xeon 8 Xeon 8
offline experiments and CPU CPU
analysis using benchmarks
from SPEC-CPU 2000 suite,
namely mcf and perl.
• While mcf has high memory VM 4 VM 4 VM 4 VM 4
accesses per cycle (MPC) VPCU VCPU VPCU VCPU
and low instructions per
cycle (IPC), perl has low
MPC and high IPC.
Homogeneous ( ‘same’)
Intel Quad Intel Quad
Xeon 8 CPU Xeon 8 CPU
VM 4 VM 4 VM 4 VM 4
VPCU VCPU VPCU VCPU
perl perl perl perl perl perl perl perl mcf mcf mcf mcf mcf mcf mcf mcf
Heterogeneous (‘mixed’)
Intel Quad Intel Quad
Xeon 8 CPU Xeon 8 CPU
VM 4 VM 4 VM 4 VM 4
VPCU VCPU VPCU VCPU
perl perl perl perl mcf mcf mcf mcf mcf mcf mcf mcf perl perl perl perl
Results of motivation experiments
Conclusion of motivation experiments
• co-scheduling VMs with similar characteristics is not
beneficial from energy efficiency and power consumption
balance point of view at high utilization rates
• co-scheduling VMs with heterogeneous characteristics on
the same PM is beneficial from both energy efficiency and
performance point of view
vGreen Design: Architecture
vgnode
• vgnode: A vgnode refers to an
individual PMin the cluster. Each
vgnode has vGreen modules (vgxen
and vgdom) installed on them.
• vgxen is a module compiled into Xen
and is responsible for characterizing
the running VMs.
• vgdom is to communicate with vgserv
Hierarchical Metrics in vGreen
• vgxen starts the
CPU counters of that
PCPU to count the
following events:
1. Instructions Retired
(INST)
2. Clock cycles (CLK)
3. Memory accesses
(MEM).
MPC (MEM/CLK)
IPC (INST/CLK)
Util (VCPU on PCPU)
• wMPCcur= MPC · util
• wIPCcur = IPC · util
Estimated wMPC:
vgserv
• vgserv: managing VM scheduling
across the vgnode cluster.
• vgpolicy makes the scheduling
decisions based on VM metrics from
the vgnodes. The metrics of each VM
are aggregated by the vgpolicy to
construct the top level or node level
metrics (nMPC, nIPC, nutil)
• vgpolicy runs its balancing algorithm
periodically
Balancing Algorithm: MPC balance
• 1) MPC balance:
This step ensures
that nMPC is
balanced across all
the vgnodes in the
system for better
overall performance
and energy efficiency
across the cluster
Balancing Algorithm
• 2) IPC balance: This step ensures nIPC is balanced
across the vgnodes for better balance of power
consumption across the PMs. The algorithm is similar to
MPC balance, but uses nIPC instead of nMPC.
• 3) Util balance: This step balances the utilization of
vgnodes to ensure there are no overcommitted nodes in
the system, if there are other underutilized vgnodes.
• 4) VM consolidation: If all the metrics in the prior three
steps are balanced, then this step tries to consolidate low
utilization VMs to generate idle vgnodes in the system,
which could be then be moved into low power states.
Evaluation Platform
Vgserv:
vgnode1: vgnode2: Core2Duo
Intel Quad Xeon 8 CPU Intel Quad Xeon 8 CPU Desktop
Xen3.3.1 Xen3.3.1 Linux
2.6.23
Pp_period = 5s
Pup_period = 5s
nMPCth = 0.02
VM 3 VM 4
Dom0 VM1 VM2 Dom0
4 VPCU 4 VPCU nIPCth = 8
Xen-Linux 4 VPCU 4 VPCU Xen-Linux
512MB 512MB
2.6.18 512MB 512MB 2.6.18
benchmark benchmark
Benchmarks Used
PARSEC benchmarks are parallel and multi-
threaded
SPEC benchmarks are single threaded.
Each VM consists of four instances (for SPEC) or
four threads (for PARSEC) of the benchmark.
Compare to Eucalyptus (E+)
• Eucalyptus is an open source cloud computing system, that
can manage VM creation and allocation across a cluster of
PMs. The default Eucalyptus VM scheduler assigns VMs using
a greedy policy, i.e. it allocates VMs to a PM until its resources
(number of CPUs and memory) are full.
• However, this assignment is static, and it does not perform any
dynamic VM migration based on actual PM utilization at
runtime.
• For fair comparison, we augment the eucalyptus scheduler with
the utilization metrics and utilization/consolidation algorithms
proposed in the previous section, which allow it to
redistribute/consolidate VMs dynamically at run-time.
• We refer to this enhanced scheduler as E+
Parameter of results: Energy savings
• 1) Energy savings: We estimate the energy reduction in
executing each combination of VMs using vGreen over E+.
This is calculated by measuring the total energy
consumption for a VM combination with E+ and vGreen,
and then taking their difference. Note, that the
combinations may execute for different times with E+ and
vGreen, and since we do not know the state of the system
after the execution (could be active if there are more jobs,
or be in sleep state if nothing to do), we only compare the
energy consumed during active execution of each
combination.
Parameter of results:
Weighted Speedup (WS)
• Talonei is the execution time of VMi when it runs alone on a
PM, and Te+i and Tvgreeni are its execution time as part of a
VM combination with E+ and vGreen respectively.
• we normalize Te+i and Tvgreeni against Talonei for each VM,
and then take ratio of the sum of these normalized times
across all the VMs in the combination
Parameter of results: Reduction in Power Imbalance
• 3) Reduction in Power Imbalance: We also estimate
the%reduction in power imbalance in the system with
vGreen due to IPC balancing compared to E+. We
estimate this using %reduction in variance in power
consumption of the two vgnodes. A high value for this
metric indicates better power balance across the two
vgnodes with vGreen.
What’s happening during balance
vgnode1 vgnode2 • Initial configuration
VM1 VM2 VM 3 VM 4 • Vgserv detect MPC imbalance
MPC MPC IPC IPC • Migrate VM1 to vgnode2
vgnode1 vgnode2
• Vgserv detect IPC and
VM2 VM 3 VM 4 VM1 utilization imbalance
MPC IPC IPC MPC • Migrate VM3 to vgnode1
• Final configuration
Homogeneous Workloads
• We also experimented with combination of VMs running
homogeneous benchmarks. We did experiments for all
the six benchmarks in Table 2, where all the fours VMs
ran the same benchmark. We observed that in all the
experiments, there was no possibility of rebalancing
based on characteristics, since the MPC and IPC of the
VMs were already balanced.
• Consequently, the results for all the three parameters
were the same as that for E+ across all the experiments.
Overhead
• Negligible overhead in read and report counters.
• Negligible performance impact of live migrating.
Conclusion
• In this paper we presented vGreen, a system for energy
efficient computing in virtualized environments. The key
idea behind vGreen is linking workload characterization to
VM scheduling decisions to achieve better performance,
energy efficiency and power balance in the system. We
designed novel hierarchical metrics to capture VM
characteristics, and develop simple balancing policies to
achieve the above mentioned benefits. We implemented
vGreen on a real life testbed of state of the art server
machines, and show with SPEC and PARSEC
benchmarks, that it can achieve improvement in
performance and system level energy savings by up to
20% and 15% respectively over state of the art policies.
Insights
• Typically we classify applications as IO-consuming and
CPU-consuming, while in this paper, all CPU-consuming
benchmarks are divided into MPC( memory consuming )
and IPC( instruction consuming ). MPC is also a kind of IO
operation but is counted in CPU usage. Heterogeneous
workloads placement can improve both efficiency and
power reduction.
• Parameter collecting running as Xen modules
Compare vGreen with
what we are doing currently
Common Difference
• implemented in physical • vGreen use network
testbed communication between vgdom
and vgserver while we use
• Manage and monitor VMs status files stored in NFS.
running on PMs cluster • vGreen classify CPU-usage as
• Creating VMs MPC and IPC while we do not
• balancing using live migration • vGreen collect cpu-usage data
• Take both efficiency and power from xen as modules, while we
try to use application level utility
consumption into consideration
• We make priority among VMs
• Server / client architecture while vGreen treat all VMs
• Use CPU-consuming equally
benchmarks • We assign resource (currently
CPU) among VMs while vGreen
• Consolidate after balancing leave this work to Xen.
