efficient resource management for cloud computing environments
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Efficient Resource Management for Cloud
Computing EnvironmentsAndrew 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 conflictingo 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 supercomputerso performance has
doubled > 3000 timeso performance per watt
has doubled 300 timeso performance per square
foot has doubled 65 times
Scaling
• Dynamic Voltage and Frequency Scaling (DVFS)o Intel SpeedStepo 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 Cloudo VM Schedulingo VM Image Managemento Data Center Design
• Schedulingo Placement within cloud
infrastructureo Energy use of server
equipment, datacenter temperature important
• Image Managemento Small Sizeo Few unnecessary proce
sses/serviceso Migrationo Dynamic Shutdown
• Data Center Designo More efficient A/C,
power supplieso Hot and cold aisleso Utilizing external cooling
Virtual Machine Scheduling
• Thermal-Awareo Minimize overall
temperatureo 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 neededo 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 serverso Can be used on machines
idle during scheduling
Virtual Machine Image
• Operating systems are designed to run on diverse hardwareo Not the case in the cloudo Normal for Linux to spend 15 seconds in modprobeo Reducing delay times, disabling modules can cut this
down significantly• Graphical User Interfaces
o Generally not necessary for cloud machineso Increase boot timeo Increase size of image significantly
• Boot order profileo Balance CPU utilization, I/O throughout entire booto bootchart
• Readahead
Power Consumption Analysis
• OpenNebulao 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 frontso Faster startup/shutdown increases effectiveness of
dynamic startup/shutdowno Smaller images are easier to migrate, require less
network traffico 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/idUS275708584920110802
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