This document discusses energy awareness in cloud computing. It notes that the IT industry accounts for 2% of carbon emissions and that improving efficiency is needed. The document outlines characteristics of cloud computing and discusses how datacenters concentrate computation. It also explores topics like virtualization, power management, task consolidation, resource scaling, load balancing, and developing power efficient software to reduce energy usage in cloud systems. The goal is to build energy aware computing systems that only use required resources.

1. University of St Andrews
School of Computer Science
Energy Aware Clouds
St Andrews Cloud Computing co-laboratory
James W. Smith
jws7@cs.st-andrews.ac.uk

2. University of St Andrews
School of Computer Science
Justification
• 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
• Energy Aware Computing
– reducing power on chips
– cooling
– build efficient systems
– software?
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3. University of St Andrews
School of Computer Science
Cloud Computing
• Defined by characteristics:
– On Demand Self-Service
– Broad Network Access
– Resource Pooling
– Rapid Elasticity
– Measured Service
• Datacentres
– Concentrated Centres of Computation
– Always on
– Cost effective?
• Nearly every major corporation in IT has interest in Cloud
Computing...
– $150bn market by 2013? 3

4. University of St Andrews
School of Computer Science
Is this new?
John McCarthy (1961):
“computation may someday be organised as a public utility”
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5. University of St Andrews
School of Computer Science
Is this just Grid Computing?
Grids Clouds
On demand Self-Service
Broad Network Access
Resource Pooling
Rapid Elasticity
Measured Service
Disclaimer: I didn’t come up with this, Ian Foster et al did...
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6. University of St Andrews
School of Computer Science
One man & a credit card
Can now access one of the largest computing resources in the world
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7. University of St Andrews
School of Computer Science
Datacentres
• Smart Construction
– Location, Location, Location
• Monitoring
– Tough Job (Yi)
• Power Usage Effectiveness
– Total Facility Power / IT Equipment Power
• Cooling
– Is the massive amount of cooling required a good thing or a bad thing?
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8. University of St Andrews
School of Computer Science
Cooling
• Why do we need to cool?
– Preserve lifetime of components
• Mechanical Engineering
– Air or water?
– Direct Heat Exchange
• Computer Science
– Smart load balancing?
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9. University of St Andrews
School of Computer Science
Virtualization
• Virtualization makes clouds run
– Run multiple VMs on each physical machine
– Improves utilization, cost effectiveness
• Save Energy
– Increase Utilization
– Migrate work?
• Clouds
– Can we save even more energy? S.E.P.
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10. University of St Andrews
School of Computer Science
Energy-Aware Computing
• Cost of purchase is now exceed by cost of operation
– Enterprise is not good at estimating operational costs
– And it varies with workload...?
• So how do we construct Energy Aware Systems?
– Power Down
– Consolidate Tasks
– Scale Resources
– Balance Work Smartly
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11. University of St Andrews
School of Computer Science
Power Management
• Migrate Components between Power States
• How much do we switch off?
– Laptop analogy
• Sending to sleep still costs energy
• Shutting down would save more at the cost of additional time
• Performance & Response Times vs. Energy Savings
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12. University of St Andrews
School of Computer Science
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?
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13. University of St Andrews
School of Computer Science
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
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14. University of St Andrews
School of Computer Science
Load Balancing
• Traditional model
– Distribute work evenly
– Each node has equal workload
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15. University of St Andrews
School of Computer Science
Load Skewing
• Energy efficient model
– “Skew” load
– Give work to nodes while they can handle it
– Power down unused nodes
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16. University of St Andrews
School of Computer Science
Power Efficient Software
• 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?
• Principles:
– 1) Work done corresponds to resources consumed
– 2) Event based rather than polling
– 3) Take care with memory
– 4) Batch Additional resource requests
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17. University of St Andrews
School of Computer Science
Future Work
• Virtualisation
– Measure performance derogation
– Energy savings?
– Is the power cloud more efficient?
• Modify Allocation algorithms
– Taking into consideration Energy-Aware principles
• Power Efficient Software
– Experiment to see if its possible
– Draw up guidelines
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18. University of St Andrews
School of Computer Science
Questions?
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