Efficient Resource Management for Cloud Computing Environments

1. Efficient Resource Management for Cloud Computing Environments 2: Rochester Institute of Technology 102 Lomb Memorial Drive Rochester, New York 14623 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. Outline • Introduction • Motivation • Related Work • Green Cloud Framework • VM Scheduling & Management • Minimal Virtual Machine Images • Conclusion & Future Work

3. 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 large-scale 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

4. 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

5. Cloud Computing • Features of Clouds • Scalable • Enhanced Quality of Service (QoS) • Specialized and Customized • Cost Effective • Simplified User Interface

6. 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?

7. 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.

8. 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

9. 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.

10. Framework

11. VM scheduling on Multi-core Systems • 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 Scheduling Power consumption curve on an Intel Core i7 920 Server (4 cores, 8 virtual cores with Hyperthreading)

12. 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

13. 485 Watts vs. 552 Watts VM VM VM VM VM VM VM VM Node 1 @ 170W Node 2 @ 105W Node 3 @ 105W Node 4 @ 105W VS. VM VM VM VM Node 1 @ 138W Node 2 @ 138W VM VM VM VM Node 3 @ 138W Node 4 @ 138W

14. 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

15. VM VM VM VM 1 Node 1 Node 2 VM VM VM VM VM 2 Node 1 Node 2 VM VM VM VM 3 Node 1 Node 2 VM VM VM VM 4 Node 1 Node 2 (offline)

16. 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

17. 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. • Built for Xen DomU • No 3d graphics, no sound, minimalistic kernel • Build modules within kernel directly VM Image Design

18. 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

19. 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.

20. 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.

21. Appendix

22. 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 Compared”

23. 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.

24. Minimal VM Image Ubuntu Linux • 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 Vs. Minimal Ubuntu

25. 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”

26. Performance Impact of VMs

27. DVFS VM Scheduling