Efficient Resource Management for Cloud Computing Environments Andrew

Efficient Resource Management for Cloud Computing Environments Andrew J. Younge 1, Gregor von Laszewski 1, Lizhe Wang 1, Sonia Lopez-Alarcon 2, Warren Carithers 2 1: Pervasive Technology Institute Indiana University 2719 E. 10 th Street Bloomington, Indiana 47408 2: Rochester Institute of Technology 102 Lomb Memorial Drive Rochester, New York 14623

Outline • • Introduction Motivation Related Work Green Cloud Framework VM Scheduling & Management Minimal Virtual Machine Images Conclusion & Future Work 2

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 Mc. Carthy, 1961 • “Cloud computing is a largescale 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 3

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 4

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

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 CO 2 produced by burning fossil fuels. • What if we could reduce the energy used with minimal performance impact? 6

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 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 CO 2 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. 7

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 8

Research Opportunities • There 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. 9

Framework Green Cloud Framework Virtual Machine Controls Scheduling Data Center Design Management Server & Rack Design Power Aware Thermal Aware VM Image Design Migration Air Cond. & Recirculation Dynamic Shutdown 10

VM scheduling on Multi-core Systems 180 Scheduling 170 160 150 Watts • 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 140 130 120 110 100 90 0 1 2 3 4 5 6 Number of Processing Cores 7 8 Power consumption curve on an Intel Core i 7 920 Server (4 cores, 8 virtual cores with 11 Hyperthreading)

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 12

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

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 14

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

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 16

Cloud Linux Image • Start with Ubuntu 9. 04. • Remove all packages not required for base image. – – No X 11 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. VM Image Design – Built for Xen Dom. U – No 3 d graphics, no sound, minimalistic kernel – Build modules within kernel directly 17

Energy Savings • Reduced boot times from 38 seconds to just 8 seconds. – 30 seconds @ 250 Watts is 2. 08 wh or. 002 kwh. • In a small Cloud where 100 images are created every hour. – Saves. 2 kwh of operation @ 15. 2 c per kwh. – At 15. 2 c per kwh this saves $262. 65 every year. – In a production Cloud where 1000 images are created every minute. – Saves 120 kwh less every hour. – At 15. 2 c per kwh this saves over 1 million dollars every year. • Image size from 4 GB to 635 MB. – Reduces time to perform live-migration. – Can do better. VM Image Design 18

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 19 separately.

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. 20

Appendix 21

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” 22

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. 23

Minimal VM Image • 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 Ubuntu Linux Vs. Minimal Ubuntu 24

VM Scheduling • Implemented scheduler on Open. Nebula 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” 25

Performance Impact of VMs 26

DVFS VM Scheduling 27