green cloud computing

University of St Andrews

School of Computer Science

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Energy Aware Clouds

James W. Smithjws7@cs.st-andrews.ac.uk

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

3

Costs

• Operational costs exceeding purchase costs

• Mainly driven by energy costs

• Even over a relatively short lifespan

University of St Andrews

School of Computer Science

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so who benefits?

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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University of St Andrews

School of Computer Science

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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!

University of St Andrews

School of Computer Science

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)

University of St Andrews

School of Computer Science

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?

University of St Andrews

School of Computer Science

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...

University of St Andrews

School of Computer Science

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?

University of St Andrews

School of Computer Science

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University of St Andrews

School of Computer Science

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University of St Andrews

School of Computer Science

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Is this new?

John McCarthy (1961):

“computation may someday be organised as a public utility”

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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Cheap power isn’t always green

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

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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

University of St Andrews

School of Computer Science

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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?

University of St Andrews

School of Computer Science

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Research Areas

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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? 2

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Speed then time and power

University of St Andrews

School of Computer Science

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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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University of St Andrews

School of Computer Science

Load Balancing

• Traditional model– Distribute work evenly

– Each node has equal workload

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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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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?

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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

University of St Andrews

School of Computer Science

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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

University of St Andrews

School of Computer Science

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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?

University of St Andrews

School of Computer Science

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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?

University of St Andrews

School of Computer Science

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Questions?