This document discusses energy efficiency in cloud computing. It begins by noting that the IT industry accounts for 2% of global carbon emissions and that 50-75% of this is from powering devices. The author then discusses how operational costs of data centers now exceed purchase costs due to rising energy prices. Several techniques for improving energy efficiency are proposed, including virtualization, task consolidation, load balancing, and profiling virtual machines to match them with servers based on resource requirements. The goal is to increase server utilization and turn off unused servers to reduce overall energy usage.

1. University of St Andrews
School of Computer Science
Energy Aware Clouds
or how I’m becoming a hippie, commie, tree
hugging, kool-aid drinking, buzzword spouting PhD
candidate...
James W. Smith
jws7@cs.st-andrews.ac.uk
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2. University of St Andrews
School of Computer Science
Introduction
in 2007
Total Carbon Footprint of the IT industry was 2% of all human ac;vity
•830 MtCO2e
•Depending upon who you believe
•Energy powering devices is 50‐75% of this total
•Need to build sci‐fi power or improve efficiency
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3. University of St Andrews
School of Computer Science
Introduction
in 2007
Total Carbon Footprint of the IT industry was 2% of all human ac;vity
•830 MtCO2e
•Depending upon who you believe
•Energy powering devices is 50‐75% of this total
•Need to build sci‐fi power or improve efficiency
However, that’s only the
official reason for saving
energy...
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4. University of St Andrews
School of Computer Science
Costs
Operational costs exceeding purchase costs
•Even over a relatively short lifespan
•[3-5] years
•Mainly driven by energy costs
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5. University of St Andrews
School of Computer Science
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6. University of St Andrews
School of Computer Science
SO WHO BENEFITS?
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7. University of St Andrews
School of Computer Science
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8. University of St Andrews
School of Computer Science
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9. University of St Andrews
School of Computer Science
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10. University of St Andrews
School of Computer Science
Is this new?
“computation may someday be
organised as a public utility”
John McCarthy (1961)
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11. University of St Andrews
School of Computer Science
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
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12. University of St Andrews
School of Computer Science
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 12

13. University of St Andrews
School of Computer Science
Cheap power isn’t always green
• Allow me to be a hippie for a second...
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14. University of St Andrews
School of Computer Science
Power Usage Effectiveness
• PUE compares how much energy is used by computing
and infrastructure equipment
PUE = Total Facility Power / IT Equipment Power
• Perfect efficiency would give PUE of 1.0
• Most datacentres in the range 1.3 -> 3.0
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15. University of St Andrews
School of Computer Science
Green Cloud?
Positive Negative
• Datacentres can become the • Datacentres are now
most efficient centres for consuming 0.5% of all
computation yet electricity in the world.
• Providers will want to • This will only continue to
increase cost effectiveness grow!
• and be green!
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16. University of St Andrews
School of Computer Science
Private Cloud
• However, Enterprise does have concerns about
Cloud systems which Private Clouds can help to
address
– Security
– Privacy
– Administrative Control
Private Cloud Systems have been likened to
“drinking on your own and calling it a private party”
- P. Laudenslager
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17. University of St Andrews
School of Computer Science
StACC Private Cloud
• So when the StACC cloud works
what does it offer?
– a platform for experimentation
....
lover
on tro
C architecture longitivtiy
workloads #of nodes
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18. 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?
– Power down unused machines
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19. University of St Andrews
School of Computer Science
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...
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20. University of St Andrews
School of Computer Science
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?
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21. University of St Andrews
School of Computer Science
Monitoring
• Reports have estimated that only 13.4% of organisations monitor their
energy consumption!
• Each component in a system must expose their consumption
information
• If such functionality doesn’t exist then 3rd party tool needed
• A controller can use this information to manage the system
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22. University of St Andrews
School of Computer Science
Feedback
Places
Cloud
VMs
Controller
On
Resource
Utilisation
Information
Monitor data
Power
Distribution
Unit Server Nodes
Modified Private Cloud infrastructure 22

23. 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
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24. University of St Andrews
School of Computer Science
Load Balancing
• Traditional model
– Distribute work evenly
– Each node has equal workload
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25. University of St Andrews
School of Computer Science
Load Skewing
• Possible efficient model
– “Skew” load
– Give work to nodes while they can handle it
– Power down unused nodes
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26. University of St Andrews
School of Computer Science
Taking it further...
CPU CPU DISK
VM VM VM
Server Server
Allocated with respect to maximising server
utilisation
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27. University of St Andrews
School of Computer Science
Taking it further...
CPU CPU DISK
VM VM VM
Server Server
Allocated with respect to maximising server
utilisation
is this really the best solution?
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28. VM Profiles
• Use the monitoring information to create “Profiles” for
each VM
VM’s do one task!!!1
• Is this VM
– CPU/Disk/Memory/Network intensive?
• Software model profiles VM according to resource
consumption and energy usage
• Now assign by Cloud Controller according to profile....
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29. University of St Andrews
School of Computer Science
Taking it even further...
CPU DISK CPU
VM VM VM
Server Server
Allocated according to VM Profile
Benefits:
- Simple system
- No teleportation of VMs
- utilization increase in multiple dimensions
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30. University of St Andrews
School of Computer Science
and in the end...
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31. University of St Andrews
School of Computer Science
and in the end...
we save a wee bit of energy...
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32. University of St Andrews
School of Computer Science
and in the end...
we save a wee bit of energy...
and live happily ever after.
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33. University of St Andrews
School of Computer Science
Questions?
Credits
Diagrams & slides by me (jws7@cs.st-andrews.ac.uk)
Photos from Google Image search
and one slide nicked from Ali Khajeh-Hosseini
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