VBRV IDEO SOURCE CHARACTERIZATION AND AP RACTICAL HIERARCHICAL MODEL István Cselényi a, SándorMolnár
a
DepartmentofNetwork Services,
Vitsandsgatan 9B,S-12386,
b
b
TeliaResearch AB
Farsta,Sweden,E-mail:
[email protected]
High Speed NetworksLaboratory,Dept.ofTelecommunicat
ionsand Telematics,
BudapestUniversity oTechnology f and Economics Pázmány Péter sétány 1/D,H-1117,Budapest,Hungary,E-mail: molnar@ttt-a
tm.ttt.bme.hu
Abstract TherearemanywaystobuilduptrafficmodelsforVBR istousemathematicalanalysisbasedonrealisticas accordingtoastochasticprocess.Inthiscase,the comparing statisticstroesultsobtained from measureme Inthispaper,wechooseadifferentandmorepractica source.Ourmodelbuildingphilosophyisthatwe
videosources.Afrequentlyappliedmethodology sumptionstosetupasourcemodelthatgeneratestraffi criticalissueisthevalidationofthesynthetict ntsotnhe real source. approach l tomodelthebehaviorotfherealtraffic analyzeandunderstand
whathappenswiththevideo
informationonitswayfromtheingresstothemulti
mediaterminaltotheegressofthenetworkcard.
Throughoutthisjourneytheinformationips rocessedby
severalmechanismsandwbeuildanempiricalmodel
stepbsytepbased oonurmeasurement-based observations.
c raceby
I. Cselényi,S.
Molnár VBR / Video SourceCharacterizationAndA Practical
Besidesunderstandingthetrafficgenerationprocedure
HierarchicalModel
statistical , analysisoV f BRtraffictracescaptured
from naumberofvideo sequenceswasalso carried outi
nseveralscenarios.Usingtheknowledgeof
encoding,
encapsulation and scheduling processesand resultsoth f
terace analysis,a
issetup
formodeling the multimediaterminal.Thereby ourmode
imitates l the generation ovideo f framesandthei
nner
tsroeproduce the complexbehavioroftherealsource.W
e
working oeach f level ofprotocol hierarchy and trie usethe
leakybucketanalysis
forverificationofthemodelinordertocapturedi
hierarchicalsourcemodel
rectlythebehaviorotfhe
trafficiqueue. an
Keywords: ATM traffic characterization,VBR
videosourcemodel,
hierarchical modeling, whitebox
modeling,IPoverATM
1. Introduction VariableBitRatevideohasanimportantroleinbroad forecasttrafficips roducedbymultimediasources(e.g. etc.).ThecharacteristicsofVBRvideotrafficbase problemshave been oincreasing f interestin the last
bandInternet,becauseasubstantialportionof teleconferencingterminals,video-on-demandserver donmeasurementstudiesandtherelatednetworking decade of
SourcemodelsoVBR f videoareneededtodimensionn
umberodf ifferentmodelshavebeenproposedfor
anoverviewsee[9,25]).Thelargevarietyom f odeling
intothefollowingthreecategories: models[18,28].However,all
Markovmodels[1,17],
blackboxapproach
alternativemethoditsheso-called
processicshosenandthe
atisticsotfherealsource.Thismethodologycanbe
w , hichisbasedmerelyonthecharacteristicsofme whiteboxapproach
approachescanbedivided
autoregressiveprocesses[10,23]andfractal
commontotheseapproachesthatsapecificstochastic
parametersaresetbyaspecificmethodtofitsomest regardedasa
teletrafficresearch [8,11,14,19,22].
etworksandcontrolmethodstoachieveacceptable
qualityandoptimalusageonf etworkresources[4,8].An VBRvideomodeling(for
s,
asuredtraffic.An
which , attemptstoreproducethedetailedbehaviorof
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I. Cselényi,S.
Molnár VBR / Video SourceCharacterizationAndA Practical
thesourcebyimitatingitsinnerworking[12].Thisme modelingcommunitysofar.However,webelievethatt practice,becauseican t capturetheimpactofencodinga traffic.Therefore wceombine the white and black boxm
thodologyhasreceivedlittleattentionfromthevi
structureanalysis,silenceperiodanalysisonseveral blackboxanalysisifsollowedbyawhiteboxanalysisth multimediaterminal.Ontheotherhand,weintroduce
ndencapsulationproceduresonthegenerateddata odeling approachesitnhiswork. ndwegiveacomprehensive
sourceanalysis
byperformingtrafficintensityanalysis,correlation burstlevelsrangingfrompackettoscenelevel.This atisaimedadetecting t theinternal apractical,
builtupusingtheobservationsandparametersidentified describeasimpletechniquetomodeltheencodingand
deo
hismodelingconceptcanbeverysuccessfulin
Themaincontributionofourpaperistwofold.Ononeha study andrevealtheverynatureomeasured f videotraffic
HierarchicalModel
behaviorotfhe
hierarchical,videosourcemodel
which , is
duringtheblackandwhiteboxanalysis.We schedulingovf ideoframesandemulatetheimpactof
layersitnhe protocol stack otnhe ATMtraffic. Themainadvantagesofourproposedmodelbuildingtechn
iquecomparedtopreviousmodelsarethe
following: •
•
Wemaptheresultsoblack f boxtrafficanalysistourk
nowledgeothe f trafficgenerationprocedure.
Thiswhite boxapproach yields model a whose behavioris
very close ttohatofthe real source.
Wepresentarelativelysimplealgorithmfortraffic
generationwheretheparameterscanbeasilyset
based om n easurements. •
The model targetstcoapture directly the queuing
behavior(i.e.
lengthandcellloss)oftherealsource.Weavoid
leaky bucketor
burstinesscurve,queue
the complexityoffittingdifferentstatistical
characteristics and investigating rather a complexqueuing model. •
Themodelingconceptisverifiedbycomparingthequeuin traffictraces.Werestricttheuseofstatisticala
gperformanceothe f syntheticandcaptured ssumptionsaboutthetrafficandsetourmodel
parametersdirectly from measurements.
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Molnár VBR / Video SourceCharacterizationAndA Practical
Thispaperisorganizedasfollows.
HierarchicalModel
Themeasurementscenario,investigatedmultimediaplat
sequencesareintroducedinSection2We . exploretheVB itstrafficadt ifferenttimescalesinSection3.T
Rsourcefromoutsideaablack s boxbyanalyzing hanwelookinsidethebox(i.e.whiteboxapproach)and
identifytheunitswhichhavesignificantimpactont hierarchicaltrafficgenerationmodelfrom theblack
formandvideo
heresultanttrafficinSection4.Weconstructa andwhiteboxanalysisinSection5The . verification
application othis f model are given iS n ection 6Fina .
and
lly,conclusionsare summarized iS n ection 7.
2. TrafficMeasurement 2.1 MeasurementScenario Avideocassetterecorderprovidedvideoandaudiosig procedure.Theopticalsignalfromthemultimediaserve testequipmentwithoutaffectingthebehavioroftheappl interestwasrecordedinreal-timebyamoduledevelo instrumentdevelopedintheRACEPARASOLproject
nalsinordertohavearepeatablemeasurement was r copiedbymeansoan f opticalsplittertoATM icationinuse.The pedby
interarrivaltimeoATM f cellsof
TeliaAB,Sweden.ItresidesinanATMtest
[3].Thetrafficrecordswerepost-processedwith
software developed btyhe authors. SunSPARC10workstationsequippedwithvideo,audiohardw endstations.Permanentvirtualconnectionswereest protocolstack.
areandATMinterfacecardswereusedas ablishedamongthemwithclassicalIPoverATM
IPdatagramwereencapsulatedusingIEEE802.2LLC/SNAPan
usingAAL5.NoshapingwasappliedtotheATMcellstrea layer.Thevideoandaudioinformationweremultiplexed platform,desktopmultimediaapplication,videocardand frequentlyusedasamultimediaterminalofreasonable characterization omultimedia f sourcesicnase odis f movie teleswitching ovideo r odnemand.Evaluation omeas f
dsegmentedintoATMcells
mwhichwascarriedoverSTM-1SDHphysical onasingleVirtualChannel.Theworkstation codingschemeweusedinourinvestigationsare price.Thisscenarioisadequatetotraffic tributed multimediaapplicationssuchavsideoconference, urementsfordifferentplatformsiispnrogress.
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Molnár VBR / Video SourceCharacterizationAndA Practical
HierarchicalModel
2.2 VideoSequences Several standard CCIRvideo sequencesrecorded iV nH
Squality wereusedavsideosource[5,6].Threeof
them were selected forthispaper: • GirlWithToys
A sequence oalmost f still pictures–no motion dynamic
• Sussie
Head and shoulderscene,no cameramovements
• Popple
Zoom ofmoving object
Thevideosequenceswerecodedusing
s – low motion dynamics
– high motion dynamics
CellBcompressionandsegmentedbythedesktopmultimed
application stohat caonstantrate oframes f ovary f
ingsizeswasgenerated.The
is low-cost a video compression scheme used mostlyon
Sunplatform
compressionalgorithmssuchaM s PEGorH.261,ithasthe variableoutputvideorate.Theaudioinformationwasco bitrate.The casesexamined itnhispaperare given in
ia
CellBcompressionalgorithm
[26].Incomparisonwithstandardvideo
advantageocf heaphardwaresupportandless dedandtransferredinastreamof64
Kbpsconstant
Table 1The . lastcolumn othis f tablepresentsthelon
term average rate,which idsenoted by
g-
R.
Notation
NameoVideo f Sequence
GT10
Girl with Toys
SU10
Sussie
10
384x288
37.17
582.75
PL10
Popple
10
384x288
24.04
1500.01
GT25
Girl with Toys
SU25
Sussie
25
384x288
34.55
1026.76
PL25
Popple
25
384x288
35.19
2251.11
Table 1
FrameRate [frame/sec] 10
Resolution [pixels] 384x288
25
384x288
Length of Trace[sec] 21.41
35.89
AverageRate[Kbps] 452.93
922.26
Parametersothe f Measured VideoSequences
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2.3 Traditional Analysisof VideoSequences TheSquaredCoefficientoV f ariation(SCV)oC f ell
InterarrivalTimes(CITs)isusedasthe
measure.Thisdescriptorand othertraditional traffi
scource statisticsare listed in
burstiness
Table 2.Thevaluesconfirm
that the burstinessothe f trafficids eterminedbythevideocontentandiis t
independentoftheframerate
ThustheGT10-GT25andSU10-SU25sequenceshavealmostthesa
me burstiness.However,the
ofPL25icsonsiderablylessthanPL10’s;probablyduetoth
burstiness
seaturationotferminalperformance.Itcanbe
seen from the peak cell rate valuesthatno shaping wa
aspplied.
Nameof Stream
CITmean [cell time]
CITvariance
GT10
342.89
9077900
77.21
366792
22871
SU10
266.59
6997300
98.52
366792
51089
PL10
103.54
2309000
215.38
366792
85060
GT25
168.40
235300
78.82
366792
78066
SU25
151.20
2115300
92.53
366792
83696
PL25
68.99
Table 2
.
818380
Burstiness
171.93
Peak Cell Rate [cell/sec]
366792
TrafficCharacteristicsothe f Captured Cell Stream
Numberof captured cells
186827
s
3. SourceAnalysis InthissectionwceonsidertheVBRsourceaablack s anunknownmechanism.Differentmethodsareappliedon fromcellleveltoscenelevel.Asaresultofthed generation procedure inside othe f black boxand obtain s
boxandanalyzetheATMcellstreamproducedby themeasureddataadifferent t timescalesranging etailedanalysis,wemakesomehypothesisaboutthetr
affic
everal trafficparametersthatare used ionurmodel.
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HierarchicalModel
3.1 TrafficIntensityAnalysis The usual way otraffic f characterization itsm o easur
ietsintensity,i.e.the
giventimeslot.Thetrafficintensityorfecorded ordertoinvestigatetheburststructure.
ATMtraffictracesisanalyzedadt ifferenttimesc Figure1-4showtrafficintensityoasfhorttracefr
sequenceondifferentburstlevels.Eachcolumnrepresen
The complete PL25sequence ishown in
Figure 1The . two level shiftsare caused btywo
Figure 1
inthepicturefield.
a) ndshowsthearrivalpatternosfevenvideoframe
audio packetsibnetween.The internal structure ofafr we show saingle packetin
omthePL25video
.
periodswithanintermediateperiodopf artialmotion videoframelevel
ales,in
tsthenumberoafrrivalsinonetimeslotof58330,
750,38and one cell timesiF n igure 1,2,3and 4respectively ,
scale(i.e.
amountofcellarrivalswithina
ame (i.e.
Figure2magnifiesthenexttime osvf aryingsizewiththree
IPpacketlevel
Figure 4thatcontains172ATMcellsarriving apractic t
TheCell Arrival IntensityoThe f PL25Sequenceon Scene
Length oWindow: f 38sec,Mean Ratein TheWindow:2251
highspeedzoom
is )shownin
Figure3Finally, .
ally full link rate.
Level
Kbps,SizeoTime f Slot:58330cell times
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I. Cselényi,S.
Molnár VBR / Video SourceCharacterizationAndA Practical
audio
Figure 2
TheCell Arrival IntensityoThe f
Length oWindow: f 490ms,Mean Ratein TheWindow:3.26
Figure 3
TheCell Arrival IntensityoThe f
Length oWindow: f 24.5ms,Mean Ratein TheWindow:9.45
audio
HierarchicalModel
audio
PL25Sequenceon VideoFrameLevel Mbps,SizeoTime f Slot:750cell times
PL25Sequenceon IP acketLevel Mbps,SizeoTime f Slot:38cell times
8
I. Cselényi,S.
Figure 4 Length oWindow: f 654
Molnár VBR / Video SourceCharacterizationAndA Practical
TheCell Arrival IntensityoThe f
pronounced,asiistshowninotherworks[5,14,24].Observ offrames,packetsandcellsadt ifferenttimescale illustratedin
PL25Sequenceon Cell Level
µs,Mean Ratein TheWindow:133.4
Basedonthetrafficintensityanalysisthemulti-le
Figure3By . plottingsimilarfiguresfortheothervid
ofvideoframesdependonthecontentofvideosequen
HierarchicalModel
Mbps,SizeoTime f Slot:1cell time
velburststructureotfheexaminedVBRtrafficiswell ingthesefiguresnoteworthyitsheregulararrival s.Audiopacketsalsoarriveinaregularmannerasit
is
eosequenceswceanconcludethatthesize cewhilethestructureoframeinternalpacketslooks
very much the same foreach frame. Basedontheseobservationsourhypothesisitshat
thegenerationoframes f andpacketsisindependent
thustheseburstlevelscanbdeistinguishedinourmodel
We . introducethefollowingnotationfordescribing
the multilevel burststructure:
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I. Cselényi,S.
Molnár VBR / Video SourceCharacterizationAndA Practical
Nf
Tip
N
Tdp
HierarchicalModel
Np*
Np
p
Tsp
Tsc Tsf
Tif
Np Tip Tdp Tsp Npaudio
packetsize N packetinterarrivaltime T packetduration T packetsilence period T size oaudio f packet T
Figure 5 Figure5depictsthe
Tdf
f if df sf sc
frame size frame interarrivaltime frame duration frame silence period cellsilence period
Multilevel Burstsin theTrafficStream
interarrivaltimeanddurationoframes f (T
periodsbetween framesand between packets(T cellsiframe na andpacket, a respectively.
if,
Tdfand ) packets(T
and Tsp),respectively. sf
Nand f
ip,
Tdpand ) thesilence
Ndenote thenumberofATM p
* thesizeothe f lastpacketinaframe,whi Npdenotes
shorterthanotherpackets(itcontainsthelastfra containinganaudiotransferunit(whichim s uchsmall use the same notation foraudio traffic,although “fra Intherestofthischapter,wefurtheranalyzetheV
chius sually
gmentoftheframe),while
Npaudioreferstothepacket
erthanavideopacket).Forthesakeosfimplicityw
e
me”doesnotexistin thatcase. BRtrafficandtrytoquantifytheseparameters,whic
h
will contribute tourtrafficmodel.
3.2 SilencePeriodAnalysis Besidethetrafficintensityanalysis(i.e.analysi information aboutthe trafficitscoalculate the probabi (CIT).Thislattermethod can bceonsidered aasnaly
sofbusyperiods)theothernativewayofgaining lity massfunction(PMF)oftheCell sisothe f silence periods(T
Theprobabilitiesareestimatedbycountingtheoccurrenc trace.Thevaluesaresmoothedbyamovingaveragete depicted in Figure 6and the complementary probability density functi
eof
sf,
InterarrivalTimes Tsp and Tsc).
CITsodf ifferentlengthsinthecaptured
chniquebeforedrawingfigures.ThePMFofCITsis on (CPDF)ispresented in
Figure 7.
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Tsp
HierarchicalModel
Tsf
−6
GT25
6 3 0
SU25
6
6
PL25
0x 10−6
5000
10000
15000
20000
25000
0x 10−6
5000
10000
15000
20000
25000
0
5000
10000 15000 20000 Cell Interarrival Time [cell time]
25000
3 0
3 0
Figure 6
x 10
ProbabilityMassFunction oCell f Interarrival
Thesilenceperiodscanbdeividedintothreegroupsac
cordingto
SU25andPL25sequences.Thelongestinterarrivaltimes periods(T
silentperiodsinside the packets(T
Figure6which , characterizestheGT25,
(above8000celltimes)representtheframesilence
Themediumvalues(around4000celltimes)correspondto sf).
frames,i.e.between consecutive packets(T
Times(GT25,SU25,PL25)
thesilenceperiodswithinthevideo
) the smallestvalues(below 10celltimes)express spwhile
theshort
The evaluation oeach f groupigsiven below. sc).
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Molnár VBR / Video SourceCharacterizationAndA Practical
−2
GT25
−3
PL25
GT−PL25
10
10
HierarchicalModel
SU25 −4
10
0
5000
10000 15000 20000 25000 30000 35000 40000
−2
GT−PL10
10
GT10 SU10
−3
10
PL10 −4
10
0
Figure 7
5000
10000 15000 20000 25000 30000 35000 40000 Cell Interarrival Time [cell time]
ComplementaryDistribution Function oCell f Interarrival (GT10,SU10,PL10,GT25,SU25,and PL25)
Times
3.2.1 Frame Level IncaseoGT-PL10 f sequences,thevideoframerateis time(T
10frame/secthusthetheoreticalframeinterarrival
around3679celltime.FortheGT-PL25sequenceswit ifi)s
interarrivaltime(T
h25frame/sec,thetheoreticalframe
14672celltime.TwoarrowsinFigure8indicateth ifi)s
silenceperiodbetweenframescannotbelongerthant Figure5).However,itisclearlyshownin
esevalues.Theoretically,the
heframeinterarrivaltime(sinceT Figure7thatthere
=T
if
T - dffrom
areseveralsilenceperiodslongerthan3679
and14672celltimesfortheGT-PL10sequencesandGT-PL25s framerate,
sf
equences,respectively.Thatisinterms
theinvestigatedterminalplatformisnotabletoproduceframesathe t t
heoreticalrate
of (i.e.10
and 25fps). Moreover,themoderatedeclinationotfheCPDFcurve timeanddurationvaries.Anotherphenomenontobneot (i.e.T
)significantlyshorterforthePLsequencesthan sfmaxis
sin icedin
Figure7indicatesthattheframegeneration Figure7isthatthemaximumsilenceperiod
fortheothers,probablyduetothehighertraffic
intensity and largersize ovideo f frames.
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3.2.2 PacketLevel IncaseoSU25 f sequence,theprobabilityonormal f packet lessthaninthecaseoPL25 f (see
silenceperiod(3000
