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Efficient Resource Management for Cloud Computing Environments . 2: Rochester Institute of Technology 102 Lomb Memorial Drive Rochester, New York 14623. Andrew J. Younge 1 , Gregor von Laszewski 1 , Lizhe Wang 1 , Sonia Lopez-Alarcon 2 , Warren Carithers 2.

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efficient resource management for cloud computing environments

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

outline
Outline
  • Introduction
  • Motivation
  • Related Work
  • Green Cloud Framework
  • VM Scheduling & Management
  • Minimal Virtual Machine Images
  • Conclusion & Future Work
what is cloud computing
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
virtualization
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
cloud computing
Cloud Computing
  • Features of Clouds
    • Scalable
    • Enhanced Quality of Service (QoS)
    • Specialized and Customized
    • Cost Effective
    • Simplified User Interface
data center power consumption
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?
motivation for green data centers
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.
green computing
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
research opportunities
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.
vm scheduling on multi core systems
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)

power aware scheduling
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

485 watts vs 552 watts
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

vm management
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

slide15

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)

minimizing vm instances
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

cloud linux image
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

energy savings
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

conclusion
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.
future work
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.
cloud computing22
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”

data center design
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.
minimal vm image
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

vm scheduling
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”