green cloud computing

University of St Andrews

School of Computer Science

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Energy Aware Clouds

James W. [email protected]



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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



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Costs

• Operational costs exceeding purchase costs

• Mainly driven by energy costs

• Even over a relatively short lifespan



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so who benefits?



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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 5



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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



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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!



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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)



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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?



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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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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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Is this new?

John McCarthy (1961):

“computation may someday be organised as a public utility”



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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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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



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Cheap power isn’t always green

• Allow me to be a hippie for a second...



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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



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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



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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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Research Areas



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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



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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



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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



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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? 2

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Speed then time and power



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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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Load Balancing

• Traditional model– Distribute work evenly

– Each node has equal workload

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Load Skewing

• Energy efficient model– “Skew” load

– Give work to nodes while they can handle it

– Power down unused nodes

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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?

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PES Principles

1. Useful work corresponds to resources consumed

2. Event-based architecture over polling

3. Light on memory

4. Batch I/O requests

Software Modularity?

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My Work



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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



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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?



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Research Question

• Can Cloud Computing have a positive impact on the energy efficiency of IT systems & can private clouds be made more energy efficient?



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Questions?

green cloud computing

Technology

energy costs

energy increase utilization

equipment power

problems of energy efficiency

country energy consumption

scifi power

virtualization virtualization

energy aware clouds