MarketBasedOp-miza-on:
Applica-ons
20September,2016
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on
MBO:Overview
MBO
Buyandsell
Agentsmaximize
profit
• 
• 
• 
• 
Usedtodescribeadistributedsystem
U-lisesagentsandtheir‘fitness’
Decentralisedsystemforlargecomplexity
Agentscancooperateoroperateindependently
•  Agentsinteracttobothbuyandsellresources
•  Resourcescanbeanythingdependingontheproblem
•  Individualagentsarefocusedonmaximisingtheirown
‘profit’
•  Systemthereforetendstowardsanop-maldistribu-on
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-2
MBO:Overview
Biddingpolicy
•  AnofferofseTngapriceoneiswillingtopayfor
something
•  Biddingcanbeuniqueordynamic
Auc-ons
•  Phases:Announcement,BiddingandClearing&Winner
determina-on
•  Types:Sealed-bid,Double,Walrasian
Auc-onmechanism
selec-on
•  Situa-onal
•  Besttobekeptsimple
•  Ifmorethanoneresource,mul-pleauc-onscanoccur
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-3
Summaryofexamples
Resourcealloca-on
•  Appliedtodistributedcompu-ng
•  U-lisesacon-nuousdoubleauc-on(CDA)
•  Discussesnego-atonwithinmarkets
Loadbalancing
•  Describesawaytoexchangejobsbetweenserversina
loadbalancertodistributetheloadasmuchaspossible
overmanyservers
•  MBOisusedprovideaserverpairingmechanismto
avoidequilibriumwhenbenefitscouldbemade
Trafficcontrolsystems
•  Usedinanurbanroadscenario
•  UsestheWalrasianauc-onmethod
•  ShowshowMBOcanshowmarketscomparedtotraffic
lights
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-4
ResourceAlloca-on
inGridCompu-ng
GridCompu-ng
•  Aformofdistributedcompu-ng
•  Nodesaregeographicallydistributedandcan
beheterogeneous
•  Looselycoupled,soli_leifany
communica-ondirectlybetweennodes
•  Forexample:SETI@Home
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-6
Agents
ResourceProducers
•  Representsthecomputa-on
nodesofthegrid
•  ​𝑅↓𝑖 =(​𝑐↓𝑖 ,​𝑠𝑡↓𝑖 ,​𝑤𝑙↓𝑖 ,​𝑟↓𝑖 ,​𝑚𝑝↓𝑖 )
–  𝑐​ ↓𝑖 −computa-onalspeed(inMI/
s)
–  ​𝑠𝑡↓𝑖 −star-ng-mefornewly
acceptedjobs(basedoncurrent
workload)
–  ​𝑤𝑙↓𝑖 −stored​𝑠𝑡↓𝑖 fromthelast
-meajobwasallocatedtoit
–  ​𝑟↓𝑖 −minimumprice(reserve)
–  ​𝑚𝑝↓𝑖 −maximumprice
CITS4404Ar-ficialIntelligence&Adap-veSystems
ResourceConsumers
•  Representsusers
•  Eachhasoneormorejobs
•  ​𝐽↓𝑖 =(​𝑙↓𝑖 ,​𝑏↓𝑖 ,​𝑑↓𝑖 )
–  𝑙​ ↓𝑖 =lengthofjob,inmillions
ofinstruc-ons(MI)
–  ​𝑏↓𝑖 =budget
–  ​𝑑↓𝑖 =deadline
•  Jobsmustbecompleted
withinbudgetbeforethe
deadline
MarketBasedOp-miza-on-7
Con-nuousDoubleAuc-on(CDA)
•  Producersplaceasks
•  Consumersplacebids
•  Tradesoccurwhen𝑚𝑎𝑥
𝑏𝑖𝑑≥𝑚𝑖𝑛 𝑎𝑠𝑘
•  Marketpriceistheaverage
ofmaxbidandminask
•  Tradesthatarepossibleat
themarketpriceoccur
CITS4404Ar-ficialIntelligence&Adap-veSystems
Bids
(BuyOrders)
Asks
(SellOrders)
Amount Price
Price Amount
10
17.0
18.5
15
15
16.5
19.0
30
40
16.0
19.5
20
20
15.5
20.0
15
30
15.0
20.5
25
MarketBasedOp-miza-on-8
U-lityFunc-ons(PriceperMI)
ResourceProducers
•  Askpricebasedoncurrent
workload
–  Resetstomaximumwhena
newjobisaccepted
–  Reducesover-me,reaches
minimumwhenallqueued
jobsarefinished
CITS4404Ar-ficialIntelligence&Adap-veSystems
ResourceConsumers
•  Bidpricebasedon
availabilityofResource
Producersand-meun-l
deadline
–  ResourceProducer
consideredavailableiffitcan
completejobwithindeadline
–  FeweravailableResource
Producersresultsinahigher
price
–  Priceincreasesas-meun-l
deadlinedecreases
MarketBasedOp-miza-on-9
U-lityFunc-onPlots
ResourceProducers(𝝈=𝟓)
CITS4404Ar-ficialIntelligence&Adap-veSystems
ResourceConsumers()
MarketBasedOp-miza-on-10
Results
•  Outperforms:
–  Firstcome,firstserved
–  Shortestjobfirst
–  Earliestdeadlinefirst
–  Incen-vebased
•  Performancecanbe
furtherimprovedusing:
–  Morecomplexu-lity
func-ons
–  Differentmarketmodels
CITS4404Ar-ficialIntelligence&Adap-veSystems
ExtractedfromIzakianetal.
MarketBasedOp-miza-on-11
Nego-a-on
1.  JobbroadcastedtoResource
Producers
2.  AvailableRPsrespond
3.  EachRPnego-ateswithRC
4.  Bestfinalpricegetsthejob
•  Allowsu-litytobecalculated
onaperjobbasis
•  Canreacttonumberof
compe-tors
–  Morecompe-torsresultsin
higherconcessionrates
ExtractedfromAdabietal.
•  Moresuitablewhen
requirementsforjobsvary
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-12
LoadBalancing
LoadBalancing
Twoagentmarket:
clients‘buy’serverresources
servers‘sell’serverresources
h_ps://assets.wp.nginx.com/wp-content/uploads/2015/10/uneven-hash-distribu-on.png
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Methodology
•  Keyassump-ons:
–  Jobshaveapredicted
ini-alcost
–  Jobscanbetransferred
betweenserverswhile
running
•  Basicalgorithm:findtwo
bestserverstoexchange
jobswith
•  Selec%onpolicy:
whichjobsshouldbe
exchanged?
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-15
SimpleSelec-onPolicies
• 
• 
• 
Latestarrivaljob(LAJ)
–sendthelastreceivedjob
Backfilllowest(BL)
–  fillthetarget’s
leastusedresource
Backfillbalance(BB)
–  minimisetarget’s
maximumload/averageload
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MarketBasedOp-miza-on-16
Market-basedselec-onpolicy
•  Addanaddi-onalexchanger
statetopreventbadmarket
equilibrium
•  Keymetricforpairing:loadimbalancedegree
•  Secondarymetric:resourcecapacity
–  maximise where
-
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-17
Improvementscenario
Before
CITS4404Ar-ficialIntelligence&Adap-veSystems
Aner
MarketBasedOp-miza-on-18
Results
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-19
Urbanroadtrafficcontrol
systems
Urbanroadtrafficcontrolsystems
Fig.1.Urbanroadnetworkusedfortheevalua-on
O=originordes-na-onpointsoutsidethecitycenter
C=cri-calintersec-onpointsconnectedthefreewaysgoingdowntownwiththeringroad
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Walrasianauc-on
Atagiven-met,tradingbeginwithpricevectorPt
quan-tyofresourcesrequired
Setofbuyerβ Actualprice
Setofsupplierϒ
quan-tyofgoodsprovided
Suppliercomputedemand/supply
Excesssupply->currentP
Excessdemand->currentP
Supplier->newpricevectorPt+1->buyers
Transac-ontakesplaceatequilibriumpricep*
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-22
Intersec-onmanagerpriceupdate
Algorithm
01:whiletruedo
02:
forallincominglinkthatisamemberofintersec-onjdo
03:
evaluatedemand
04:
Excessdemand/supply=demand–supply
05:
newlinkprice=currentlinkprice
*totaldemandatcurrentprice/supply
06.
endfor 07:
incrementt
08:endwhile
Note:
Supplyofthatlinkisaconstantand-meindependent
Totaldemand=numbersofvehicleswillingtocrossthatincominglinkatapar-cularprice
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Fundamentaldiagramoftrafficflow
Fig.2.Fundamentaldiagramoftrafficflow.
Legend:
=theassumedop-maldensitythatmaximisesthetrafficflowonlink
=lengthoflink
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on-24
Compe--vemarketvstrafficlights
Averagetravel-me=
CITS4404Ar-ficialIntelligence&Adap-veSystems
Averagespeed=
MarketBasedOp-miza-on-25
Vehicledensityvaria-on
Fig.3.Vehicledensityvaria-onover-meforcri-calintersec-ons.
(e)Cri-calintersec-onc5and(f)Cri-calintersec-onc6.
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Fig.1.Urbanroadnetworkusedfortheevalua-on.
Furtherenhancements/poten-alexpansionofthis
applica-on
1.  Discountsforusageatapar-cular-me
2.  Dailysubscrip-ons
3.  Real-mereportoftheunexpectedconges-on
4.  Automa-cinfringement-cketsforspeeding
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MarketBasedOp-miza-on-27
MarketBasedOp-miza-on:
Applica-ons
20September,2016
CITS4404Ar-ficialIntelligence&Adap-veSystems
MarketBasedOp-miza-on
References
•  Izakian,H.,Abraham,A.,&Ladani,B.T.
(2010).Anauc-onmethodforresource
alloca-onincomputa-onalgrids.Future
Genera%onComputerSystems,26(2),228-235.
•  Adabi,S.,Movaghar,A.,Rahmani,A.M.,&
Beigy,H.(2013).Market_basedgridresource
alloca-onusingnewnego-a-on
model.JournalofNetworkandComputer
Applica%ons,36(1),543-565.
Appendix:Jobexchangingmaths
Costfunc-onofaddingajob
Benefitofexchangingajob