Green Cloud Computing
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On June 24th I presented to the Dependable Systems Engineering group here in the School of Computer Science, St Andrews. The group meets once a month for a presentation from one of its members over ...

On June 24th I presented to the Dependable Systems Engineering group here in the School of Computer Science, St Andrews. The group meets once a month for a presentation from one of its members over lunch. The presenter talks about their current research, providing a good opportunity to keep up to date with other work within the group.On June 24th I presented to the Dependable Systems Engineering group here in the School of Computer Science, St Andrews. The group meets once a month for a presentation from one of its members over lunch. The presenter talks about their current research, providing a good opportunity to keep up to date with other work within the group.

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Green Cloud Computing Presentation Transcript

  • 1. Energy Aware Clouds
    • James W. Smith
    • [email_address]
  • 2. Introduction
    • Total Carbon Footprint of the IT industry was 2% of all human activity in 2007
      • 830 MtCO2e
      • Energy powering devices is 75% of this total
      • Need to build sci-fi power or improve efficiency
    • IT is beginning to learn that cutting emissions and cutting costs go naturally together
  • 3. Costs
    • Operational costs exceeding purchase costs
      • Mainly driven by energy costs
      • Even over a relatively short lifespan
  • 4. so who benefits?
  • 5. Roadmap
    • Energy Aware Computing
    • Cloud Computing
      • Private Clouds
      • Virtualisation
    • Datacentres
      • PUE & Productivity
      • Cooling
    • Research areas for Energy Efficient Cloud Computing
      • Monitoring
      • Resource Scaling
      • Smart Load Balancing
      • Task Consolidation
    • Power Efficient Software
    • Future Work
  • 6. Energy Aware Computing
    • Attempting to address problems of energy efficiency in Computing Systems
      • processor chips
      • cooling
    • The overall problem is to “minimise energy used to perform a certain piece of useful work”
      • Control resource availability
      • Reduce consumption
  • 7.  
  • 8. Green Cloud? Positive Negative
    • Datacentres can become the most efficient centres for computation yet
    • Providers will want to increase cost effectiveness
    • and be green!
    • Datacentres are now consuming 0.5% of all electricity in the world .
    • This will only continue to grow!
  • 9. Private Cloud
    • Private Cloud Systems have been likened to
    • However, Enterprise does have concerns about Cloud systems which Private Clouds can help to address
      • Security
      • Privacy
      • Administrative Control
    “ drinking on your own and calling it a private party” - P Laudenslager, (unknown)
  • 10. Virtualization
    • Virtualization makes clouds run
      • Run multiple VMs on each physical machine
      • Improves utilization, cost effectiveness
    • Save Energy
      • Increase Utilization
      • Migrate work?
      • Power down unused machines
      • Allocated tasks appropriately?
  • 11. Virtualization (2)
    • Performance overhead
      • intermediate layer
      • increased complexity
    • Different tasks have different performance costs
      • for example, using the same physical disk for two or more VMs...
      • and different power consumptions...
  • 12. Virtualization (3)
    • VMs increase utilization, power consumption & heat on a physical machine
    • So we need to be careful how much virtualization we do, where we do it and how we prepare for it
    • Is it possible to virtualize in an efficient manner?
  • 13.  
  • 14.  
  • 15. Is this new? John McCarthy (1961): “ computation may someday be organised as a public utility”
  • 16. Datacentres
    • The age of the datacentre is here
    • One man and a credit card can tap into some of the largest computing resources in the world
  • 17. Some figures
    • Datacentres in the USA consume 1.5% of all electricity in that country
    • Energy consumption in this area has doubled in the period 2000-2006
    • Only 50% of electricity consumed can be attributed to useful work done by servers, rest goes on cooling, infrastructure etc
    United States Environmental Protection Agency (EPA) 2007
  • 18. Cheap power isn’t always green
    • Allow me to be a hippie for a second...
  • 19. Power Usage Effectiveness
    • PUE compares how much energy is used by computing and infrastructure equipment
    • Perfect efficiency would give PUE of 1.0
    • Most datacentres in the range 1.3 -> 3.0
    PUE = Total Facility Power / IT Equipment Power
  • 20. Datacentre Productivity
    • PUE is useful but it doesn’t determine productivity over power
    • Step in the Datacentre Productivity Measurement:
    • Useful, as EAC likes to think of doing a task for least amount of power
    • But how would you measure Useful work?
    Datacentre Productivity = Useful Work / Total Facility Power
  • 21. Cooling
    • Why do we need to cool?
      • Preserve lifetime of components
    • Mechanical Engineering
      • Air or water?
      • Direct Heat Exchange
    • Computer Science
      • Smart load balancing?
  • 22. Research Areas
  • 23. Monitoring
    • Reports have estimated that only 13.4% of organisations monitor their energy consumption!
    • Each component in a system must expose their consumption information
      • and control mechanisms?
    • If such functionality doesn’t exist then 3rd party tool needed
      • Yi Yu
      • additional complexity
      • Software? Hardware?
    • A controller can use this information to manage the system
  • 24. Combining Computation and Cooling
    • Traditionally, Cooling & Computation are controlled independently
    • Cooling uses CRAC units to cool datacentre to optimum operating temperature
    • Computational load is distributed to give best performance
    • However, Parolini et al suggest that workload can be distributed smartly according to temperature
      • requires unified framework
    “ Reducing Data Center Energy Consumption via Coordinated Cooling and Load Management” - Parolini, et al 2008
  • 25. Powering Management
    • Switch off your lights!!!
      • Well, at least migrate your systems between power states
    • How much do we switch off?
        • Laptop
          • sending to sleep still costs energy
          • shutting down save more at the cost of additional time
    Performance & Response Time vs. Energy Savings
  • 26. Resource Scaling
    • Use only the amount of resource required to complete a task
      • Give each task a deadline
      • Only give resources to allow completion within that deadline
    • Speed Scaling
      • Adjust CPU speed
      • Save energy & cooling costs
    • Fine for individual components, but how do we do this on a system-wide scale?
    Speed then time and power
  • 27. Task Consolidation
    • Keep machines well utilised
    • Bin packing problem
      • Tasks are objects
      • Servers are bins
      • Resources are dimensions
    • Relies upon being able to accurately predict tasks resource requirements
      • performance adjusting applications?
  • 28. Load Balancing 14 University of St Andrews School of Computer Science
    • Traditional model
    • Distribute work evenly
    • Each node has equal workload
  • 29. Load Skewing 15 University of St Andrews School of Computer Science
    • Energy efficient model
      • “ Skew” load
      • Give work to nodes while they can handle it
      • Power down unused nodes
  • 30. Power Efficient Software 16 University of St Andrews School of Computer Science
    • Different devices consume different amounts of energy doing (roughly) the same task.
      • i.e. Making a call, playing a song
      • Why? Difference in hardware & Difference in software implementation
    • Is it possible to produce energy efficient software?
      • Optimise for time, scalability, robustness, but energy?
  • 31. PES Principles
    • Useful work corresponds to resources consumed
    • Event-based architecture over polling
    • Light on memory
    • Batch I/O requests
    Software Modularity?
  • 32. My Work
  • 33. StACC Private Cloud
    • So when the StACC cloud works what does it offer?
      • a platform for experimentation
    • We can control
      • architecture
      • longitivity
      • number of nodes
      • exact workload
  • 34. Future Work
    • Monitor VM performance
      • Performance and Energy Consumption
      • Write Resource Monitoring Software
    • Energy-Smart Control Algorithms for Clouds?
      • Based on what? Utilisation? Consumption? Mix?
    • Modify Eucalyptus open source software?
  • 35. Research Question
    • Can Cloud Computing have a positive impact on the energy efficiency of IT systems & can private clouds be made more energy efficient?
  • 36. Questions?