Cost and Energy Reduction Evaluation for ARM Based Web Servers
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Transcript of Cost and Energy Reduction Evaluation for ARM Based Web Servers
Cost and Energy Reduction Evaluation for ARM Based Web Servers
13.12.2011 Åbo Akademi University - Department of Information Technologies 1
Olle Svanfeldt-Winter, Sébastien Lafond, Johan Lilius
Sébastien [email protected]
OutlinesMotivationsEnergy consumptionTotal energy consumptionEnergy propositional computingData Centers CostsEvaluated HWBenchmarksResultsConclusion
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MotivationsEnergy consumption of data centers is both an economical and environmental issue– important impact on the possibility to construct or
expend data centres– cooling infrastructures are expensive
Models for data centre costs exists – It is possible to determine the relationship between the
total cost of a data center and the energy consumption of its server
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Energy consumptionThe two main metrics are:– PUE =
– DCiE =
Both express the energy efficient of the data center
But at the end the total energy consumption matters– this is what you will pay for every month– infrastructure design based on the maximum power
dissipation of the data center
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tPowerITequipmenityPowerTotalFacil
1001×
PUE
Total energy consumptionAccording to [1] the processors in a typical server contributed to:– 45% of the total power dissipation at peak
performance– 27% when idle
Power dissipation is application specific, but on average the dissipation is 72% of the peak power
Google server containers are reported to house 1160 servers and dissipate 250KW each.
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[1] L. Barroso and U. Holzle, “The case for energy-proportional computing,” Computer, vol. 40, no. 12, pp. 33–37, December 2007.
Energy propositional computingIdeally the energy consumption of data centers should be proportional to the required performance– this is unfortunately far from being true– energy efficiency is best at peak performance– however typical servers operate most of
the time at 10 to 50% of their capacity
Using low-end and cheaper processors might be an answer for better energy proportionality– increase the granularity of power management steps
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Data Centers CostsBased on Hamilton analysis [1,2]
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[1] J. Hamilton, “Cooperative expendable micro-slice servers (cems): Low cost, low power servers for internet-scale services,” in Proceedings of CIDR 09, January 2009.[2] “Overall data center costs. http://perspectives.mvdirona.com/2010/09/18/overalldatacentercosts.aspx,” James Hamilton, September 2010
• 10 year amortization time• 4 year amortization time for the network• 3 year amortization time for the server
• $0,07 per KWh• 80% average load usage• 50k servers• 165 W per server• 5% cost of money
Evaluated HWVersatile Express– Quad-core Cortex A9– 1GB DDR2– 400Mhz
Tegra 250– Dual-core Cortex-A9– 1GB DDR2– 1Ghz
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BenchmarksAutobench and Apache 2 HTTP server– static web pages
SPECweb2005– more demanding web services
Erlang– micro benchmarks– real world SIP proxy
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ResultsApache
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ResultsSPECweb2005
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ResultsErlang
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Calls per dissipated Watt
ConclusionThe performance of 2 ARMv7 based ARM cortex-A9 was measured and evaluated and compared to Xeon processors
Measurements show that the Cortex A9 can be up to– 11 times more efficient with the Apache server
• Enabling a 12,7% total cost saving
– 3.6 times more efficient with Erlang base SIP proxy• Enabling a 10% total cost saving
– 2.9 times mote efficient with the SPECweb2005• Enabling a 9% total cost saving
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Questions ?
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