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FAWN A Fast Array of Wimpy Nodes* Bogdan Eremia, SCPD

FAWN A Fast Array of Wimpy Nodes* Bogdan Eremia, SCPD *by DavidAndersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee,LawrenceTan,Vijay Vasudevan. Energy in computing. • Power is a significant burden on computing. •. 3-yearTCO soon to be dominated by power. Hydroelectric Dam. 2.

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FAWN A Fast Array of Wimpy Nodes* Bogdan Eremia, SCPD

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  1. FAWN AFastArrayofWimpyNodes* Bogdan Eremia, SCPD *by DavidAndersen, JasonFranklin, MichaelKaminsky, AmarPhanishayee,LawrenceTan,VijayVasudevan

  2. Energyincomputing •Powerisasignificantburdenoncomputing • 3-yearTCOsoontobedominatedbypower HydroelectricDam 2

  3. “Google’spowerconsumption...wouldincuran annualelectricitybillofnearly$38million” [Qureshi:sigcomm09] “Energyconsumptionby…datacenterscouldnearly double...(by2011)tomorethan100billionkWh, representinga$7.4billionannualelectricitycost” [EPAReport2007] AnnualcostofenergyforGoogle,Amazon,Microsoft = Annualcostofallfirst-yearCSPhDStudents 3 Monday,October12,2009

  4. Canwereduceenergy usebyafactoroften? Stillservethesameworkloads Avoidincreasingcapitalcost 4

  5. FAWN Improvecomputationalefficiencyof data-intensivecomputingusinganarray ofwell-balancedlow-powersystems. FastArrayofWimpyNodes Traditional Server FAWN CPU CPU Disk CPU CPU Mem ()*%&'+,-#. ()*%&'+,-#. ()*%&'+,-#. ()*%&'+,-#. ()*%&'+,-#. ()*%&'+,-#. AMDGeode 256MBDRAM 4GBCompactFlash 40W 220W 5

  6. Goal:reducepeakpower FAWN TraditionalDatacenter 100% Servers 20% Power Cooling 1000W 750W 100W <100W Distribution { 20%energyloss (good) 6

  7. Overview •Background •FAWNPrinciples •FAWN-KVDesign •Evaluation •Conclusion 7

  8. Towardsbalancedsystems RebalancingOptions 1E+08 1E+07 1E+06 1E+05 1E+04 1E+03 1E+02 1E+01 Disk Seek Wasted resources DRAM Access Nanoseconds 1E+00 1E-01 198019851990199520002005 Year CPUCycle Today’sCPUsSlowerCPUs ArrayofFastStorage FastestDisks SlowCPUs Today’sDisks 8

  9. Targetingthesweet-spotinefficiency Fastestprocessors exhibitsuperlinear powerusage Speedvs.Efficiency 2500 Fixedpowercostscan dominateefficiency forslowprocessors Instructions/sec/Winmillions 2000 XScale800Mhz AtomZ500 1500 FAWNtargetssweetspot insystemefficiencywhen includingfixedcosts Xeon7350 1000 500 0 CustomARMMote 1 10 100 1000 10000 100000 Instructions/secinmillions (Includes0.1Wpoweroverhead) 9

  10. Targetingthesweet-spotinefficiency Instructions/sec/Winmillions FAWN 1000 1500 2000 2500 500 0 1 CustomARMMote 10 Instructions/secinmillions XScale800Mhz 100 1000 AtomZ500 10000 Xeon7350 100000 Today’sCPUSlowerCPUSlowCPU ArrayofFastStorageToday’sDisk FastestDisks 10 Moreefficient

  11. Overview •Background •FAWNPrinciples •FAWN-KVDesign • Architecture Constraints • •Evaluation •Conclusion 11

  12. Data-intensiveKeyValue •Criticalinfrastructureservice •Servicelevelagreementsforperformance/latency •Random-access,read-mostly,hardtocache 12

  13. FAWN-KV: OurKeyValueProposition •Energy-efficientclusterkey-valuestore •Goal:improveQueries/Joule •Prototype:Alix3c2nodeswithflashstorage • 500MHzCPU,256MBDRAM,4GBCompactFlash 13 Monday,October12,2009

  14. FAWN-KV: OurKeyValueProposition UniqueChallenges: •Efficientand fastfailover •WimpyCPUs,limitedDRAM •Flashpooratsmallrandomwrites •Prototype:Alix3c2nodeswithflashstorage • 500MHzCPU,256MBDRAM,4GBCompactFlash 14

  15. FAWN-KVArchitecture ManagesBackends ActsasGateway RoutesRequests Back-end Back-end FAWN-DS X Front-end KVRing Consistenthashing Back-end Back-end Back-end 15

  16. FAWN-KVArchitecture FAWN-DS Back-end Back-end X Front-end Back-end Back-end Back-end FAWN-KV FAWN-DS LimitedResources Avoidrandomwrites EfficientFailover Avoidrandomwrites 16

  17. {

  18. Log-structuredDatastore •Log-structuringavoidssmallrandomwrites Get Put Delete RandomRead Append FAWN-KV EfficientFailover Avoidrandomwrites FAWN-DS LimitedResources Avoidrandomwrites ✔ ✔ 18

  19. Onanodeaddition HashIndex Values H A (H,B] G B F C D Node additions, failures require transfer of key-ranges 19

  20. Nodesstreamdatarange DatastoreListStreamAtomicUpdateA ofDatastoreList Minimizeslocking fromBto ConcurrentInserts, CompactDatastore Concurrent Inserts • • Backgroundoperationssequential ContinuetomeetSLA A FAWN-KV EfficientFailover Avoidrandomwrites FAWN-DS LimitedResources Avoidrandomwrites ✔ ✔ ✔ ✔ 21 Monday,October12,2009

  21. FAWN-KV Take-aways •Log-structureddatastore •Avoidsrandomwritesatalllevels •Minimizeslockingduringfailover •Carefulresourceusebuthighperforming •Replicationandstrongconsistency •Variantofchainreplication(seepaper) 21

  22. Overview •Background •FAWNprinciples •FAWN-KVDesign •Evaluation •Conclusion 22

  23. EvaluationRoadmap •Key-valuelookupefficiencycomparison •Impactofbackgroundoperations •TCOanalysisforrandomreadworkloads 23

  24. FAWN-DSLookups QPS Watt 346 51.7 2.3 1.96 System Alix3c2/Sandisk(CF) Desktop/Mobi(SSD) MacbookPro/HD Desktop/HD QPS 1298 4289 66 171 Watts 3.75 83 29 87 •FAWN-basedsystemover6xmore efficientthan2008-eratraditionalsystems 24

  25. Impactofbackgroundops 1600 1200 800 400 1600 1200 800 400 Queriespersecond Queriespersecond 0 0 PeakCompactSplit Merge PeakCompactSplit Merge Peakqueryload 30%ofpeakqueryload Backgroundoperationshave: •Moderateimpactatpeakload •Negligibleimpactat30%load 25

  26. WhentouseFAWNfor randomaccessworkloads? TCO=CapitalCost+PowerCost($0.10/kWh) Traditional(200W) Five2TBdisks 160GBPCI-eFlashSSD 64GBFBDIMMpernode ~$2000-8000pernode FAWN(10Weach) 2TBdisk 64GBSATAFlashSSD 2GBDRAMpernode ~$250-500pernode 26

  27. !$"""" !$""" !$"" !$" !$ !"#$ !"#$ Ratioofqueryrateto 0.12*+*,%34 0.12*+*0)#35 - )*+ %&%' ! !$!$"!$"" 01)23!4&')!56+77+8-(9():; ./ *, (# cooling, "#$ 0.12*+*,-./ !$"""

  28. Conclusion •FAWNarchitecturereducesenergy consumptionofclustercomputing • FAWN-KVaddresseschallengesofwimpynodes forkeyvaluestorage • Log-structured,memoryefficientdatastore Efficientreplicationandfailover Meetsenergyefficiencyandperformancegoals “Eachdecimalorderofmagnitudeincreasein parallelismrequiresamajorredesignandrewriteof parallelcode”-KathyYelick • • • 28

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