© 2 0 1 7 U MassA m h e rstG lo b al.A llrigh ts re se rve d .

Integrative Systems EngineeringECE 697 SI

Prof. Michael Zink

Week9 Lessons17SimulationExamples

RationaleThepurposeofthislesson istogiveanoverviewonaseries ofsimulationexamples. Basedonthisexamplesitwillbedemonstratedhowavarietyofsimulations caninformdesignprocessofasystem.Theyarealsoagoodwayofevaluating (toacertainaccuracy) ifcertain requirements canbemetornot.

© 2017 UMass Amherst Global. All rights reserved. 2

Objectives

• BecomefamiliarwiththewaysimulationscansupporttheSystemsEngineeringprocess

• Beingabletoidentifywhichsimulationapproachmightbebestforevaluationaspecificsystemorsubsystem

• Understandthelimitationsofsimulations

© 2017 UMass Amherst Global. All rights reserved. 3

• Introduction• Self-created:VoDSystem• Trace-based:YouTubework• MatLab:Radarplacement• ANSOFT:Phased-arrayantennasimulation

Overview

• Simulationisanimportanttoolforthedesignprocess• Simulationisimplementingamodelovertime• WealreadyknowimportantroleofmodelsinSystemsEngineering• Increaseincomputepowerhasgivenrisetotheapplicationonsimulations

Introduction

• Motivation• Scalableadaptivestreaming• Retransmissionscheduling• Heuristics• Simulation

Self-Created: Video on Demand System

• Situationintoday’sInternet• Noguaranteedservices(e.g.bandwidth)available• Streamingmustbeperformedadaptive

• Scalability• Largeamountofusers• (Geographicallydistributed)• Heterogeneousclient(high-endPCtomobilephone)

Motivation

Scalable Adaptive Streaming = System Scalability + Content Scalability

Original 4 layer videoSubnet A

Subnet B

Internet

OriginServer

Quality on cache

Quality on cache

Quality on clients

Quality on client

Quality on client

ProxyCache

ProxyCache

• Reducelayervariationsinthevideodeliveredtotheclientvideo streamed to clientretransmitted segments

initially cached video

ProxyCache

ServerClient

Retransmission Scheduling

Laye

r

Time (Segmente)

s(v1)=12.55Segments requestedfor retransmission

Segments (retransmitted) 1 2 3 4 5 6 7

Complete search (seconds) 0.0003 0.0054 0.1222 2.8206 56.1878 1168.5 23686.5

Spectrum 20.73 18.92 16.73 16.4 14.55 14.1 12.55

Heuristic (seconds) 5.3*10-5 5.3*10-5 5.3*10-5 5.3*10-5 5.3*10-5 5.3*10-5 5.3*10-5

Spectrum 20.73 22.67 16.73 18.92 18.92 18.92 12.55

Example

Heuristics for Retransmission Scheduling

• OwnC++-basedsimulationEnvironment• Instanceoflayeredvideoonthecacheisrandomlycreated• Averageof1000simulations

Time (Segments)

U-LLF

U-SG-LLF U-LL-SGF

200

400

600

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1000

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0 100 200 300 400 500 600 700 800 900

Simulation for Retransmission Scheduling

• Motivation• Measurementstudy• YouTubetraffictraces• Synthetictraces• Simulations:• P2Pcaching• Proxycaching

Trace-based Simulation: YouTube Study

• YouTubeisdifferentfromtraditionalVoD• 1billionhoursofvideowatchedeveryday• AccesstoYouTubefromacampusnetwork• Influenceoncontentdistributionparadigms?• Correlationbetweenglobalandlocalpopularity?

• Methodology:• MonitorYouTubetrafficatcampusgateway• Obtainglobalpopularity• VideoCliptrafficanalysis• Trafficgeneration• Trace-drivensimulationforvariouscontentdistributionapproaches

Motivation

How YouTube Works

22.9%77.1%1718310806/03-06/07T322.3%77.7%235157205/22-05/25T222.6%77.4%129551205/08- 05/09T1

MultiSingleTotal

Per Video StatsLength(Hours)

DateTrace# of

UniqueClients212724801547

27.5%72.5%8213216209/04-09/11T4 7538

34.1%65.9%30333133601/29-02/12T5 16336

31.5%68.5%13145016803/11-03/18T6 8879

YouTube Traffic Traces

Measurement Results: Hourly Rate

• Overcomelimitationsoftrace-drivensimulations• Investigatescalabilityofdistributionsystem• Tracesgeneratedbasedonstatisticalinformation

28.4%28.2%21.0%18.9%20.4%21.9%Multi

71.6%71.8%79.0%81.1%79.6%78.1%Single

74514571022704589542024425976TotalPer video stats

19776161847051273425201553# of unique clients

242424242412Length(hours)

S6S5S4S3S2S1Trace

Synthetic YouTube Traces

• Trace-basedsimulations• Simple:onlyonecopy• Improved:multiplecopies• Availabilityofclientsfromtraces• Windows-basedavailabilityapproach

Simulation: P2P Caching

• FIFOcachereplacement• Effectivelowcostsolutionsincestorageintheorderof100GBisrequired

• HitratesquitesimilarforallthreetracescomparedtoP2Presults

Proxy Cache

Client A (time T) Client B (time

T+x)

Simulation: Proxy Caching

Simulation Results

• High-levelabstractionsufficientinthiscase• Difficulttopreventbugs• Simulationscanbeusedto“lookintothefuture” basedonexistingdata

Lessons Learned

• Motivation• Existingtestbed• Alternatives

MatLab Simulation: Radar Placement

• Plantoextendexisting4-nodetestbed• Multipleoptionstoadd2moreradarsintestbed• Whichoneisthebestsolution?• Lookingforarelativelysimplewaytoanswerthisquestion

• Note:Answersareonlyasgoodasthespecifiedrequirements!!

Motivation

Existing Testbed

Build Out: Alternative 1

Build Out: Alternative 2

Build Out: Alternative 3

Build Out: Alternative 3

• Verifysimulationbasedoninformationavailableforexistingtestbed• MatLabcomeswithalargesetofadditionaltools• Solutionthatisapplicablewherevermapinformationisavailable• Easytoextend• Easywaytodetermineaseriesofparameters

Lessons Learned

• Motivation• Microstrip patchantenna• Phase-tiltantenna

Ansoft-based Simulation: Phase-tilt Radar

3232

Powersupplies

Radome

Antenna TRModules

Advantages:• E-scaninAzimuthandM-Scanin elevation.• Dualpolarizedwithgoodcross-polarization

Limitations:• Elevation beamwidth:3.5⁰• Maximumpeak TXpower:~60Watts

Phase-tilt: Radar

A small frequency-scan array antenna composed of16 microstrip patch antenna elements was designedat 9.6 GHz, using Ansoft Designer, and then testedin a far-field compact range of the antennaslaboratory at UMASS.

The elements of the linear array are interconnectedby a series feed (microstrip transmission lines) andthe width of each patch is defined using a synthesisapproach in order to achieve -20dB sidelobe level.The antenna performs an electronic scan of 15degrees using a range frequency from 9.3-9.9 GHz.

Measured results in the plots shows a goodagreements with simulated results using ANSOFT.The mismatch of measured and simulated sidelobesis because of the fact that in the antennameasurement there was no contemplated the a largeground plane.

Antenna Simulation

The phase-tilt antenna is made up of an activelinear array and a mechanical actuator; bothallow the antenna to perform electronicscanning in azimuth and mechanical scanning inelevation.The linear array is a planar structure of 64x32elements arranged in 64 columns; each columnis made up of 32 dual-polarized microstripelements interconnected by series-fed networksin each polarization. The column is fed bydedicated T/R modules where phase andamplitude control provides beam steering,azimuth aperture power distribution and desiredpolarization in the azimuth plane.

Measured results of one column linear array (32elements) embedded in a planar array (32x18)was performed in a FF compact range. The plotsto the right shows a very good agreement of theantenna patterns for H and V with simulatedresults using ANSOFT.

-80 -60 -40 -20 0 20 40 60 80-60

-50

-40

-30

-20

-10

0

Theta (deg)Pa

ttern

dB

Radiattion Patterns of Linear Series-Fed Aperture Patched Coupled Patch Array Antenna

IdealCo-HXo-HCo-VXo-V

Measured(Range System )

Simulated (Ansoft)

Salazar, J.L, R medina, J. Knapp, and D. J, McLaughlin, 2008: Phase-tilt array antennas design for distributed radar network for weather sensing. IEEE International Symposium on Geoscience and Remote Sensing, Boston, MA.

Antenna Simulation

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00Primary Sweep

-35.00

-30.00

-25.00

-20.00

-15.00

-10.00

-5.00

0.00

Y1

Ansoft Corporation PlanarEM1RL_Simulated FLX-10

Curve Info

dB(S(Reflect:V,Reflect:V))Setup 1 : Sw eep 1

Y Component 1Imported1.0

C-RAM FF-2 is a thin, ferrite filled, siliconerubber sheet stock which has highmagnetic loss at UHF and microwavefrequencies up to X-band. It is applied tometal surfaces to attenuate RF surfacecurrents. It can be used to modifyantenna patterns, lower the Q of a cavity,act as a transmission line attenuator, andmodify the radar cross section of targets

Simulation of this magnetic material canbe performed using the Ansoft and HFSSusing the Frequency Selective Surfacemethod and a unit cell that represents aspecific area of the material. The rightplot shows a good agreement betweenmeasured results (provided by the CumingMicrowave Corporation) and the simulatedresults using Ansoft Designer.

Comparison: Measurement vs Simulation

• Motivation• VideoTranscoding• PredictiveTranscoding• Simulation

Self-Created: Video Transcoding

• Videostreaminghasmovedfromfixedbitratetoadaptivebitratestreaming(ABR).

• Example,Netflixcreatesupto120differentqualityversionstosupportABRstreaming.

Optimizing Transcoding Workflow in CDNs

http://www.fluidcast.net/features/adaptive-bitrate-streaming/

• Objective• ToreducethetranscodingandstorageresourcesrequiredtotranscodeVoDservicevideos.

• TomaintaintheperformanceofprovidingABRstreamingattheclient.

• Approach• Onlinetranscodingofvideosegmentsonlywhenrequestedbytheclient.

• Predictthesegmentsaheadoftherequesttomaintainperformanceattheclient.

Objective and Approach

1 2

4

3 2

43

Offline Transcoding

1. Video Upload

2. Transcoding Job

3. Client Request

4. Video Delivery

Online Transcoding

1. Video Upload

2. Client Request

3. Transcoding Job

4. Video Delivery

1

Transcoding Architecture

• OfflineTranscoding• VideosegmentbemadeavailablewithinacceptedServiceLevelAgreement(SLA).• AvideoofdurationD,madeavailablewithinD/stimetousersimplicatesan1/sSLA.

• OnlineTranscoding• Allthevideosegmentsaretranscodedonlywhenrequested.

• HybridTranscoding• x/ytranscoding.x%offline,y%online.• 1Seg/Resthybridpolicy.• 0/100,pureonline• 100/0,pureoffline

Transcoding Policies

• 3-dayclientsidemeasurementofadaptivebitratevideorequestsfromAkamaiTechnologies.

• 5millionvideosessions,200Kclientsand3292videoproviders.

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0 500 1000 1500 2000 2500 3000 3500 4000

CD

F

Bitrates (Kbps)

Bitrate Distribution

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0-20 20-40 40-60 60-80 80-100

% o

f Ses

sion

s

Viewing Session Length (%)

AkamaiYouTube

Data Set

0

500

1000

1500

2000

2500

3000

0.25 0.5 1 2 5 10

Peak

Byte

s to

Tran

scod

e (G

bps)

SLA

Workload Analysis: Offline Transcoding

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05/31-20:00

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Byt

es

to T

ransc

ode (

Gbps)

Time of Day

100/00/100

1Seg/Rest

Workload Analysis: Online Transcoding

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300

10/90 30/70 50/50 70/30 90/10

Pe

ak

Byt

es

to T

ran

sco

de

(G

bp

s)

Hybrid Transcoding

Workload Analysis: Hybrid Transcoding

• PredictionStep• Markovmodelusedtopredictthenextbitratelikelyrequestedbyclient.

• TranscodeStep• Ifpredictedbitratevideosegmentnotpresent,thevideosegmentis

transcoded.

• DeliveryStep• Ifpredictionwasaccurateandtranscodedintime,thendeliveredtoclient

orelseweencounterarebufferingevent.

Predictive Transcoding

• Bitratesofpreviouslyrequestedvideosegmentsareusedtopredictthenextbitrate.

• Markovmodelbasedondifferentcategories.(clients,servers,OS,networktype)

• EachcategoryhasdifferentgroupsandMarkovmodelforeachgroup.

Example Markov FSM

Markov Prediction Modell

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Perce

ntage

of T

rans

codin

g Req

uests

(%)

Time of Day

Simple PredictionMarkov Prediction on clients

Markov Prediction on ServersMarkov Prediction on Network Type

Markov Prediction on OS

Pred

ictio

n Er

ror R

ate

(%)

Prediction Analysis: Error Rate

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0.8

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Pred

iction

Erro

r Rate

(%)

Time of Day

Markov Prediction on OSMarkov Prediction on Network Type-OS

Prediction Analysis: Error Rate

• Total transcoding time• Tto t = Ttra n s c o d e + Tc o mm

Transcoding Time

• RebufferRatioistheamountofrebufferingtimeoverthelengthofthevideo.• RebufferingTime=#segments*predictionerrorrate*Ttot

0

0.05

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Rebu

fferin

g Rati

o (%

)

Time of Day

0/1001Seg/Rest

10/9050/5070/30

Performance Analysis

• Simulations comeclosetoreality• Stilladifferencebetweenboth• Somedetailsonlyrevealedthroughmeasurements• Examplehowsimulationcanaidthedesignprocess

Lessons Learned

• Examplesofsimulations• Fromsystemtocomponentbasedsimulation• Self-createdandtool-basedsimulations

Summary