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


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


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


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.


[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


Data Centers CostsBased on Hamilton analysis [1,2]


[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


BenchmarksAutobench and Apache 2 HTTP server– static web pages

SPECweb2005– more demanding web services

Erlang– micro benchmarks– real world SIP proxy


ResultsApache


ResultsSPECweb2005


ResultsErlang


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


Questions ?


Cost and Energy Reduction Evaluation for ARM Based Web Servers

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