Green Cloud Computing
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Transcript of Green Cloud Computing
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University of St Andrews
School of Computer Science
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Energy Aware Clouds
James W. [email protected]
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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
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School of Computer Science
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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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University of St Andrews
School of Computer Science
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so who benefits?
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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
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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
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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!
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University of St Andrews
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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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University of St Andrews
School of Computer Science
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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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School of Computer Science
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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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School of Computer Science
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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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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
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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
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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?
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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?