AdelNadjaranToosi Slide1/22

GeographicalLoadBalancingforSustainableCloudDataCentres

AdelNadjaranToosi

CloudComputingandDistributedSystems(CLOUDS)Laboratory,SchoolofComputingandInformationSystems

TheUniversityofMelbourne,Australia

Email:anadjaran@unimelb.edu.auHomepage:http://www.cloudbus.org/~adel/

AdelNadjaranToosi Slide2/22

OutlineØ  BriefBiographyØ  Backgrounds

Ø  GeographicalLoadBalancing(GLB)Ø  OptimalofflinealgorithmanditsintractabilityØ  AGLBframeworkforwebapplicationsØ  ResultsandPerformanceEvaluationØ  Summaryandfuturedirections

AdelNadjaranToosi Slide3/22

BiographyandResearchOverviewØ  PhD,UniversityofMelbourne,2010-2014

o  Thesis:“OntheEconomicsofInfrastructureasaServiceCloudProviders:Pricing,Markets,andProfitMaximisation”

Ø  PostdoctoralResearchFellow,UniversityofMelbourne,2015-Presento  AlgorithmsandSoftwareSystemsfor:1.  ResourceProvisioningofData-intensiveApplicationsinHybridClouds[Completed]2.  Renewable-awareandGreenClouds[Completed]3.  Software-DefinedClouds[On-going]

Ø  ResearchInterestso  DistributedSystems,CloudComputing,Software-DefinedNetworking(SDN)andNetwork

FunctionVirtualization(NFV),EnergyEfficiencyandGreenComputing,SoftComputingo  SoftwareSystemsandAlgorithmsforResourceManagementinCloudComputingand

DistributedSystems

Ø  Publicationso  27publications,15JournalArticles(11A/A*ERARanking,ACMCSUR,TCC,JCNA,FGCS,

TAAS),11Conferencepapers(CloudCom,UCC,HPCC),1BookChapter,o  h-index:15and1200+citations(SRC:GoogleScholar)

AdelNadjaranToosi Slide4/22

`

CloudComputing

100,000SEARCHESAREMADEONGOOGLEc

6,000TWEETSARESENTONTWITTERb

250SONGSAREDOWNLOADEDVIAITUNESf

3.4MILLIONEMAILSAREEXCHANGEDa

70,000LIKESAREGENERATEDONFACEBOOKe600ITEMS

ARESOLDONAMAZONda https://www.lifewire.com/how-many-emails-are-sent-every-day-1171210b http://www.internetlivestats.com/twitter-statistics/c http://www.statisticbrain.com/google-searches/d https://www.inc.com/tom-popomaronis/amazon-just-eclipsed-records-selling-over-600-items-per-second.htmle https://www.brandwatch.com/blog/47-facebook-statistics-2016/f http://www.billboard.com/biz/articles/news/1538108/itunes-crosses-25-billion-songs-sold-now-sells-21-million-songs-a-day

AdelNadjaranToosi Slide5/22

PowerHungryCloudsØ  Clouddatacentresconsumelargeamountsof

electricityo  Highoperationalcostforthecloudproviderso  Highcarbonfootprintontheenvironment

Ø  USDataCentreso  70billionkilowatt-hoursofelectricityIn2014o  =Two-yearpowerconsumptionofallhouseholdsinNewYorko  =Theamountconsumedbyabout6.4millionaverageAmerican

homesthatyearo  Projectednearly50milliontonsofcarbonpollutionperannumin

2020.–  Source:USNaturalResourcesDefenseCouncil(NRDC)

PhotoSource:http://nycyoungmen.tumblr.com/

AdelNadjaranToosi Slide6/22

RenewableEnergyØ  Cloudprovidersaims

o  Reducingenergyconsumptiono  Dependenceonbrownenergy

Ø  Renewableenergyo  On-sitegreenpowergenerationo  Google,MicrosoftandAmazon

“AmazonWebServices(AWS)hasbuiltawindfarmin2017andexceededthegoalof50%

electricalusagefromrenewableenergysources”

Ø  NotonlyDataCentreso  MonashNetZeroProject,TheUniversityof

Melbourne'sSustainabilityPlano  BitcoinMining Source:https://aws.amazon.com/about-aws/sustainability/

AdelNadjaranToosi Slide7/22

ChallengesØ  Non-dispatchable,IntermittentandUnpredictable

o  Renewableenergysources(WindandSolar)o  Poweringdatacentresentirelywithrenewableenergysourcesis

difficult

Ø  Mixedsourcesofenergyfordatacentres:o  Gridpowerorbrownenergyo  Renewableenergysourcesorgreenenergy

Ø  Challenges:o  Minimisingbrownenergyusageo  Maximisingrenewableenergyutilisation

AdelNadjaranToosi Slide8/22

GeographicalLoadBalancing(GLB)Ø  Geographicalloadbalancing(GLB)potentials:

o  Follow-the-renewablesØ  GLBapproachbenefitscloudprovidersbutitraisesaninteresting,

andchallengingquestion:

“WithlimitedorevennoaprioriknowledgeofthefutureworkloadandDynamicand

unpredictablenatureofrenewableenergysources,howtooptimisetheoverallrenewableenergyuseandcost?”

AdelNadjaranToosi Slide9/22

Example:OfflineGLBProblem

AdelNadjaranToosi Slide10/22

OptimalOfflineAlgorithmØ  Assumingthefollowinginformationisknownfora

timewindow:o  Futureknowledgeofrenewableenergyavailabilityo  Workload(i.e.,numberofrequests,arrivaltime,anddurationof

requests)

Ø  Weshowedthattheoptimalstrategyiscomputationallyintractableo  Exponentialtimecomplexityo  Formalproof

AdelNadjaranToosi Slide11/22

GLBforWebApplications

AdelNadjaranToosi Slide12/22

OverallSystemArchitecture

AdelNadjaranToosi Slide13/22

GlobalLoadBalancer(GreenLB)

AdelNadjaranToosi Slide14/22

Grid’5000Testbed

AdelNadjaranToosi Slide15/22

WorkloadTraces

Wikipedia

AdelNadjaranToosi Slide16/22

APrototypeSystem

AdelNadjaranToosi Slide17/22

RenewablePowerandElectricityPrices

Solar Wind

Combined

AdelNadjaranToosi Slide18/22

Results

Lyon

Rennes

Reims

AdelNadjaranToosi Slide19/22

ResultsSite Metric RR Capping GreenLB

LyonPowerConsumption(kWh)BrownConsumption(kWh)

Cost(€)

36.313.31.71

42.919.02.31

41.216.92.01

ReimsPowerConsumption(kWh)BrownConsumption(kWh)

Cost(€)

32.52.10.42

32.51.10.15

35.41.90.27

RennesPowerConsumption(kWh)BrownConsumption(kWh)

Cost(€)

36.49.31.23

29.72.90.39

28.32.60.35

TotalPowerConsumption(kWh)BrownConsumption(kWh)

Cost(€)

10525.73.36

10523.02.85

10521.42.63

BrownEnergy:17%and7%CostSaving:22%and8%

AdelNadjaranToosi Slide20/22

SummaryandConclusionØ  Aframeworkforcostandenergyefficientloadbalancing

o  Distributeswebapplicationrequestsamongmultipleclouddatacentres

Ø  Aprototypeandexperimentalstudiesinarealtestbedo  RealtracesofwebrequestsforEnglishWikipediao  Meteorologicaldatainthelocationofeachdatacentreto

modelsolarandwindpowergenerationØ  Uses17%lessbrownenergyandsavescostbyalmost22%in

comparisontoroundrobinpolicy.Ø  Reducescostby8%,Brownenergyby7%incomparisonamethod

byagroupofresearchersfromRutgersandPrincetonuniversities.o  LinearOptimisationo  WorkloadandRenewableEnergyPrediction

AdelNadjaranToosi Slide21/22

FutureWorksØ  GLBforothertypesofworkloads/applications

o  Scientificworkflows,Map-Reduceo  WebStickySessionso  Demandresponse

Ø  InternetofThings(IoT)o  Healthcare,SmartVehicleso  EdgeandFogComputingo  SustainabilityandReliability

Ø  Challengeo  Shapingworkloadtomatchrenewablepowersupply

v  OffloadingIoTtaskstothecorecloudsv  Trimmingapproximationanalyticsv  Schedulingdeferraltasksv  Selectivemicroservicespower-offv  Orchestrationofnetworkslices

https://erpinnews.com/fog-computing-vs-edge-computing

AdelNadjaranToosi Slide22/22

THANKYOU

Questions?

AdelNadjaranToosi Slide23/22

Results

CDFofaverageresponsetime