Efficient Resource Management
for Cloud Computing
Environments
Andrew J. Younge
Golisano College of Computing and Information Sciences
Rochester Institute of Technology
102 Lomb Memorial Drive
Rochester, New York 14623
Outline
•
•
•
•
•
•
•
Introduction
Motivation
Related Work
Green Cloud Framework
VM Scheduling & Management
Minimal Virtual Machine Images
Conclusion
2
What is Cloud Computing?
• “Computing may someday
be organized as a public
utility just as the telephone
system is a public utility...
The computer utility could
become the basis of a new
and important industry.”
– John McCarthy, 1961
• “Cloud computing is a largescale distributed computing
paradigm that is driven by
economies of scale, in
which a pool of abstracted,
virtualized, dynamically
scalable, managed
computing power, storage,
platforms, and services are
delivered on demand to
external customers over the
Internet.”
– Ian Foster, 2008
3
Virtualization
• Virtual Machine (VM) is
a software artifact that
executes other software
as if it was running on a
physical resource
directly.
• Typically uses a
Hypervisor or VMM
which abstracts the
hardware from an
Operating System
4
Cloud Computing
• Features of Clouds
– Scalable
– Enhanced Quality of Service (QoS)
– Specialized and Customized
– Cost Effective
– Simplified User Interface
5
Data Center Power Consumption
• Currently it is estimated that servers consume
0.5%of the world’s total electricity usage.
– Closer to 1.2% when data center systems are factored
into the equation
• Server energy demand doubles every 4-6 years.
• This results in large amounts of CO2 produced by
burning fossil fuels.
• What if we could reduce the energy used with
minimal performance impact?
6
Related Work
• Scheduling on Cluster
resources
– Power aware
– Thermal aware
• Data center design to
reduce Power Usage
Effectiveness (PUE)
– Cooling systems
– Rack design
• Little research in designing
efficient Cloud data centers
7
Research Opportunities
• There are a number of areas to explore in
order to conserve energy within a Cloud
environment
– Schedule VMs to conserve energy
– Management of both VMs and underlying
infrastructure
– Minimize operating inefficiencies for non-essential
tasks
– Optimize data center design
8
Framework
Green Cloud
Framework
Virtual
Machine
Controls
Scheduling
Power
Aware
Thermal
Aware
Data Center
Design
Management
VM Image
Design
Migration
Server &
Rack
Placement
Air Cond. &
Recirculation
Dynamic
Shutdown
9
VM scheduling on Multi-core Systems
180
Scheduling
170
160
150
Watts
• There is a nonlinear
relationship between
the number of
processes used and
power consumption
• We can schedule VMs
to take advantage of
this relationship in
order to conserve
power
140
130
120
110
100
90
0
1
2
3
4
5
6
7
8
Number of Processing Cores
Power consumption curve on an Intel
Core i7 920 Server
(4 cores, 8 virtual cores with
10
Hyperthreading)
Power-aware Scheduling
• Schedule as many VMs
at once on a multi-core
node
– Greedy scheduling
algorithm
– Keep track of cores on a
given node
– Match vm requirements
with node capacity
Scheduling
11
485 Watts vs. 552 Watts
V
M
V
M
V
M
V
M
V
M
V
M
V
M
V
M
Node 1 @ 170W
Node 2 @ 105W
Node 3 @ 105W
Node 4 @ 105W
VS.
V
M
V
M
V
M
V
M
Node 1 @ 138W
V
M
V
M
Node 2 @ 138W
V
M
Node 3 @ 138W
V
M
Node 4 @ 138W
12
VM Management
• Monitor Cloud usage and load
• When load decreases:
• Live migrate VMs to more utilized nodes
• Shutdown unused nodes
• When load increases:
• Use WOL to start up waiting nodes
• Schedule new VMs to new nodes
Management
13
VM
VM
VM
VM
1
Node 1
VM
VM
VM
Node 2
VM
VM
2
Node 1
VM
VM
VM
Node 2
VM
3
Node 1
VM
VM
VM
Node 2
VM
4
Node 1
Node 2 (offline)
14
Minimizing VM Instances
• Virtual machines are desktop-based
– Lots of unwanted packages
– Unneeded services
• Are multi-application oriented, not service oriented
– Clouds are based off of a Service Oriented Architecture
• Need a custom lightweight Linux VM for service
oriented science
• Need to keep VM image as small as possible to reduce
network latency
Management
15
Cloud Linux Image
• Start with Ubuntu 9.04
• Remove all packages not
required for base image
–
–
–
–
No X11
No Window Manager
Minimalistic server install
Can load language support on
demand (via package manager)
• Readahead profiling utility
– Reorder boot sequence
– Pre-fetch boot files on disk
– Minimize CPU idle time due to I/O
delay
• Optimize Linux kernel
VM Image
Design
– Built for Xen DomU
– No 3d graphics, no sound,
minimalistic kernel
– Build modules within kernel directly
16
Energy Savings
• Reduced boot times from 38 seconds to just 8 seconds
– 30 seconds @ 250Watts is 2.08wh or .002kwh
• In a small Cloud where 100 images are created every hour
– Saves .2kwh of operation @ 15.2c per kwh
– At 15.2c per kwh this saves $262.65 every year
– In a production Cloud where 1000 images are created every
minute
– Saves 120kwh less every hour
– At 15.2c per kwh this saves over 1 million dollars every year
• Image size from 4GB to 635MB
– Reduces time to perform live-migration
– Can do better
VM Image
Design
17
Conslusion
• Cloud computing is an emerging topic in
Distributed Systems
• Need to conserve energy
• Green Cloud Framework
– Power-aware scheduling of VMs
– Advanced VM & infrastructure management
– Specialized VM Image
• Small energy savings result in a large impact
18
Future Work
• Combine concepts of both Power-aware and
Thermal-aware scheduling to minimize both
energy and temperature
• Integrated server, rack, and cooling strategies
• Further improve VM Image
• Designing the next generation of Cloud
computing systems
19
Progress
• Have some prior knowledge and research in Grid
computing
• After attending Supercomputing 2008 in late
November, I realized the future exists in Cloud
computing
• Spent the past few months investigating Cloud
computing research opportunities
– Identified Green IT work and realized it was applicable to
Clouds
– Dedicated time to researching Green computing research
and developing a framework for Cloud infrastructure
20
Accomplishments
[1] G. von Laszewski, L. Wang, A. Younge, and X. He, “Poweraware scheduling of virtual machines in dvfs-enabled
clusters,” Rochester Institute of Technology, Tech. Rep.,
2009.
[2] G. von Laszewski, A. Younge, X. He, K. Mahinthakumar, and
L. Wang, “Experiment and workflow management using
cyberaide shell,” in 4th International Workshop on
Workflow Systems in e-Science (WSES 09) in conjunction
with 9th IEEE International Symposium on Cluster
Computing and the Grid. IEEE, 2009.
[3] L. Wang, G. von Laszewski, A. Younge, X. He, M. Kunze, and
J. Tao, “Cloud computing: a perspective study,” New
Generation Computing, to appear in 2010.
[4] G. von Laszewski, F. Wang, A. Younge, X. He, Z. Guo, and
M. Pierce, “Cyberaide javascript: A javascript commodity
grid kit,” in GCE08 at SC’08. Austin, TX: IEEE, Nov. 16
2008. [Online]. Available:
http://cyberaide.googlecode.com/svn/trunk/papers/08javascript/vonLaszewski- 08- javascript.pdf
[5] G. von Laszewski, F. Wang, A. Younge, Z. Guo, and M.
Pierce, “Javascript grid abstractions,” in Proceedings of
the Grid Computing Environments 2007 at SC07, Reno,
NV, Nov. 2007. [Online]. Available:
http://cyberaide.googlecode.com/svn/trunk/papers/07javascript/vonLaszewski- 07- javascript.pdf
• Accepted into 2009 International
Summer School on Grid
Computing in Nice, France.
– Supported by US Department
of Energy OSG stipend.
• Accepted as student volunteer for
2009 International
Supercomputing Conference in
Hamburg, Germany.
• First author on an extended
abstract and poster accepted to
the TeraGrid 2009 conference.
21
Appendix
22
Cloud Computing
• Distributed Systems
encompasses a wide
variety of technologies
• Grid computing spans
most areas and is
becoming more mature.
• Clouds are an emerging
technology, providing
many of the same
features as Grids without
many of the potential
pitfalls.
From “Cloud Computing and Grid Computing 360-Degree Compared”
23
Minimal VM Image
• Easier to slim down a fully
functional distro than to create
one from scratch
• Selected Ubuntu Linux
– Jaunty 9.04
– Minimal install site
– Package management software
(aptitude)
– Continuing support
VM Image
Design
Ubuntu Linux
Vs.
Cloud
Ubuntu
24
VM Scheduling
• Implemented scheduler
on OpenNebula system
• Replaced Round Robin
scheduling system with
Based on Algorithm
• Startup and Shutdown
VM Management Easily
added
From “Opennebula: The open source virtual machine manager for cluster computing”
25
DVFS VM Scheduling
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