Reading partymay2010

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

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? 2

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

University of St Andrews School of Computer Science Is this new? John McCarthy (1961): “computation may someday be organised as a public utility” 4

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... 5

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 6

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? 7

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? 8

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. 9

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 10

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 11

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? 12

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 13

University of St Andrews School of Computer Science Load Balancing • Traditional model – Distribute work evenly – Each node has equal workload 14

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 15

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 16

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 17

University of St Andrews School of Computer Science Questions? 18

Reading partymay2010

by University of St Andrews

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