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Efficient Resource Management for Cloud Computing Environments

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  1. Efficient Resource Management for Cloud Computing Environments Andrew J. Younge, Gregor von Laszewski, Lizhe Wang, Sonia Lopez-Alarcon, Warren Carithers presented by Bryan Rosander

  2. 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 Utility Computing

  3. 2005 -  0.5% of total world energy usage and 1.2% of U.S. energy usage come from data centers World usage expected to quadruple by 2020, U.S. usage doubling every 5 years More recent articles conflicting Some suggest growth is slowing/has been slower (Reuters, Koomey) Some suggest it is still increasing (Networkworld) Is the Cloud Green?

  4. In the past 15-20 years of supercomputers performance has doubled > 3000 times performance per watt has doubled 300 times performance per square foot has doubled 65 times Green Computing

  5. Dynamic Voltage and Frequency Scaling (DVFS) Intel SpeedStep AMD PowerNow! Started in laptops and mobile devices Now used in servers Scaling

  6. Green Cloud Framework

  7. Goal is to maximize performance per watt in a Cloud VM Scheduling VM Image Management Data Center Design  Scheduling Placement within cloud infrastructure Energy use of server equipment, datacenter temperature important Image Management Small Size Few unnecessary processes/services Migration Dynamic Shutdown  Data Center Design More efficient A/C, power supplies Hot and cold aisles Utilizing external cooling Green Cloud Framework (cont.)

  8. Thermal-Aware Minimize overall temperature Reduces energy used for cooling Power-Aware Minimize total power used by servers Power to servers is the larger cost Virtual Machine Scheduling

  9. Can dynamically shutdown  and start up machines as needed 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 Can be used on machines idle during scheduling Virtual Machine Management

  10. Operating systems are designed to run on diverse hardware Not the case in the cloud Normal for Linux to spend 15 seconds in modprobe Reducing delay times, disabling modules can cut this down significantly Graphical User Interfaces Generally not necessary for cloud machines Increase boot time Increase size of image significantly Boot order profile Balance CPU utilization, I/O throughout entire boot bootchart Readahead Virtual Machine Image

  11. OpenNebula open source distributed virtual machine manager scheduler provides policies for virtual machine placement Figure illustrates the CPU power savings (assuming CPU bound tasks) Power Consumption Analysis

  12. 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 Virtual Machine Image Analysis

  13. 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 Faster startup/shutdown increases effectiveness of dynamic startup/shutdown Smaller images are easier to migrate, require less network traffic Less wasted resources for the user Conclusion

  14. Resources • Koomey, Jonathan - My new study of data center electricity use in 2010. http://www.koomey.com/post/8323374335 • NetworkWorld - Report: Global data center energy use will rise nearly 20% next year. Chris Nerney. http://www.networkworld.com/newsletters/nsm/2011/092611nsm2.html • Reuters - Data Center Power Use Drops as Green IT, Recession Take Effect. Iain Thompson. http://www.reuters.com/article/2011/08/02/idUS275708584920110802