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 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