Efficient Resource Management for Cloud Computing

Environments

2: Rochester Institute of Technology102 Lomb Memorial Drive

Rochester, New York 14623

1: Pervasive Technology InstituteIndiana University2719 E. 10th Street

Bloomington, Indiana 47408

Andrew J. Younge1, Gregor von Laszewski1, Lizhe Wang1, Sonia Lopez-Alarcon2, Warren Carithers2

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 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

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 (QoS)– 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 CO2 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 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.

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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 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.

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Framework

10

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 Power consumption curve on an Intel

Core i7 920 Server (4 cores, 8 virtual cores with

Hyperthreading)Scheduling 11

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

Node 1 @ 170WNode 1 @ 170W

VMVM

VMVM

VMVM

VMVM

VMVM

VMVM

VMVM

VMVM

Node 2 @ 105WNode 2 @ 105W

Node 3 @ 105WNode 3 @ 105W Node 4 @ 105WNode 4 @ 105W

Node 1 @ 138WNode 1 @ 138W

VMVM

VMVM

VMVM

VMVM

VMVM

VMVM

VMVM

VMVM

Node 2 @ 138WNode 2 @ 138W

Node 3 @ 138WNode 3 @ 138W Node 4 @ 138WNode 4 @ 138W

485 Watts vs. 552 Watts

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VS.

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

Node 1Node 1

VMVM VMVM VMVM VMVM

Node 2Node 2

Node 1Node 1

VMVM VMVM VMVM VMVM

Node 2Node 2

Node 1Node 1

VMVM VMVM VMVM VMVM

Node 2 (offline)

VMVM

Node 1Node 1

VMVM VMVM VMVM VMVM

Node 2Node 2

1

2

3

4

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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 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 17

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 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 separately. 19

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.

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Appendix

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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

23

• Need new data center designs strategies to reduce cooling requirements.

• Pod-based clusters:• Modular• Semi-portable• Closed-loop systems

• Quebec’s CLUMEQ Silo supercomputer.

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.

Ubuntu LinuxUbuntu Linux

Minimal UbuntuMinimal Ubuntu

Vs.

VM Image Design 24

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”25

Performance Impact of VMs

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DVFS VM Scheduling

27