Efficient Resource
Management for Cloud
Computing Environments
Andrew J. Younge, Gregor von
Laszewski, Lizhe Wang, Sonia
Lopez-Alarcon, Warren Carithers
presented by Bryan Rosander
Utility Computing
• Long been a vision
• Grid computing failed to
really catch on
• Technology advances as
well as a viable business
model have helped Cloud
Computing catch on
• Cloud Computing allows
for fuller utilization of
hardware
• Energy consumption is
turning into a major issue
Is the Cloud Green?
• 2005 o 0.5% of total world
energy usage and 1.2%
of U.S. energy usage
come from data centers
o World usage expected
to quadruple by 2020,
U.S. usage doubling
every 5 years
• More recent articles
conflicting
o Some suggest growth is
slowing/has been
slower (Reuters,
Koomey)
o Some suggest it is still
increasing
(Networkworld)
Green Computing
• In the past 15-20 years of
supercomputers
o performance has
doubled > 3000 times
o performance per watt
has doubled 300 times
o performance per square
foot has doubled 65
times
Scaling
• Dynamic Voltage and Frequency Scaling (DVFS)
o Intel SpeedStep
o AMD PowerNow!
• Started in laptops and mobile devices
• Now used in servers
Green Cloud Framework
Green Cloud Framework (cont.)
• Goal is to maximize
performance per watt in a
Cloud
o VM Scheduling
o VM Image Management
o Data Center Design
• Scheduling
o Placement within cloud
infrastructure
o Energy use of server
equipment, datacenter
temperature important
• Image Management
o Small Size
o Few unnecessary proce
sses/services
o Migration
o Dynamic Shutdown
• Data Center Design
o More efficient A/C,
power supplies
o Hot and cold aisles
o Utilizing external cooling
Virtual Machine Scheduling
• Thermal-Aware
o Minimize overall
temperature
o Reduces energy used
for cooling
• Power-Aware
o Minimize total power
used by servers
o Power to servers is
the larger cost
Virtual Machine Management
• Can dynamically
shutdown and start up
machines as needed
o Similar to Condor Glide-In
(dynamically adds and
removes machines from
the resource pool)
• Live migration can move
virtual machines from lightly
loaded to medium load
servers
o Can be used on machines
idle during scheduling
Virtual Machine Image
• Operating systems are designed to run on diverse
hardware
o Not the case in the cloud
o Normal for Linux to spend 15 seconds in modprobe
o Reducing delay times, disabling modules can cut this
down significantly
• Graphical User Interfaces
o Generally not necessary for cloud machines
o Increase boot time
o Increase size of image significantly
• Boot order profile
o Balance CPU utilization, I/O throughout entire boot
o bootchart
• Readahead
Power Consumption Analysis
• OpenNebula
o open source distributed
virtual machine
manager
o scheduler provides
policies for virtual
machine placement
o Figure illustrates the
CPU power savings
(assuming CPU bound
tasks)
Virtual Machine Image Analysis
•
•
•
•
•
•
Prototype Linux image created based on Ubuntu Linux 9.04
All unnecessary and desktop-oriented packages removed
Image went from 4Gb to 636Mb
Removed many daemons, processes, and libraries
Utilized readahead to condense I/O into one burst
Boot time went from 38 seconds to 8 seconds
Conclusion
• Power savings within the Cloud are an increasingly
important area to focus on
• Power-Aware scheduling can help increase utilization,
synergizes well with dynamic shutdown and startup
• Virtual Machine Image optimization can lead to gains on
several fronts
o Faster startup/shutdown increases effectiveness of
dynamic startup/shutdown
o Smaller images are easier to migrate, require less
network traffic
o Less wasted resources for the user
Resources
1. Koomey, Jonathan - My new study of data center electricity
use in 2010. http://www.koomey.com/post/8323374335
2. NetworkWorld - Report: Global data center energy use will
rise nearly 20% next year. Chris
Nerney. http://www.networkworld.com/newsletters/nsm/2011
/092611nsm2.html
3. Reuters - Data Center Power Use Drops as Green IT,
Recession Take Effect. Iain
Thompson. http://www.reuters.com/article/2011/08/02/idUS2
75708584920110802