Efficient Resource Management
for Cloud Computing
Environments
Andrew J. Younge1, Gregor von Laszewski1, Lizhe Wang1, Sonia
Lopez-Alarcon2, Warren Carithers2
1:
Pervasive Technology Institute
Indiana University
2719 E. 10th Street
Bloomington, Indiana 47408
2:
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 & Future Work
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
Motivation for Green Data Centers
• Economic
– New data centers run on
the Megawatt scale,
requiring millions of
dollars to operate.
– Recently institutions are
looking for new ways to
reduce costs, no more
“blank checks.”
– Many facilities are are at
their peak operating
envelope, and cannot
expand without a new
power source.
• Environmental
– 70% of the U.S. energy
sources are fossil fuels.
– 2.8 billion tons of CO2
emitted each year from
U.S. power plants.
– Sustainable energy
sources are not ready.
– Need to reduce energy
dependence until a more
sustainable energy source
is deployed.
7
Green Computing
• Performance/Watt is not following Moore’s
law.
• Advanced scheduling schemas to reduce
energy consumption.
– Power aware
– Thermal aware
• Data center designs to reduce Power Usage
Effectiveness.
– Cooling systems
– Rack design
8
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.
9
Framework
Green Cloud
Framework
Virtual
Machine
Controls
Scheduling
Data Center
Design
Management
Server &
Rack
Design
Power
Aware
Thermal
Aware
VM Image
Design
Migration
Air Cond. &
Recirculation
Dynamic
Shutdown
10
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
11
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
12
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
13
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
14
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)
15
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
16
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
17
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
18
Conclusion
• Cloud computing is an emerging topic in Distributed
Systems.
• Need to conserve energy wherever possible!
• Green Cloud Framework:
– Power-aware scheduling of VMs.
– Advanced VM & infrastructure management.
– Specialized VM Image.
• Small energy savings result in a large impact.
• Combining a number of different methods together
can have a larger impact then when implemented
separately.
19
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 minimization.
• Designing the next generation of Cloud
computing systems to be more efficient.
20
Appendix
21
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
22
Data Center Design
• Need new data center
designs strategies to reduce
cooling requirements.
• Pod-based clusters:
• Modular
• Semi-portable
• Closed-loop systems
• Quebec’s CLUMEQ Silo
supercomputer.
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 profile compared
to other major distros.
– Excellent package management
software (aptitude).
– Great support.
VM Image
Design
Ubuntu Linux
Vs.
Minimal
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
Performance Impact of VMs
26
DVFS VM Scheduling
27