Electronic Visualization Laboratory University of Illinois at Chicago

EMERGE Deep Tech Mtg

Oliver Yu, Jason Leigh, Alan Verlo

Electronic Visualization Laboratory University of Illinois at Chicago

Performance Parameters

• Latency= Recv Time - Send Time• Note: Recv Host and Send Host are synchronized.

• Jitter = E[{Li - E[L]}]– Note: E[ ] is the expection of data set.

• L is the set of 100 most recent Latency samples.

• Packet Loss Rate

Electronic Visualization Laboratory University of Illinois at Chicago

Row

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0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

Latency vs. Time

20Mbps

40Mbps

60Mbps

80Mbps

Time

Late

ncy

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0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

Jitter vs. Time

20Mbps

40Mbps

60Mbps

80Mbps

Time

Jitt

er

20Mbps 40Mbps 45Mbps 50Mbps 60Mbps 80Mbps

0

0.1

0.2

0.3

0.4

0.5

Packet Lost Rate vs. Background Traffic

Background Traffic

Pac

ket

Lo

st R

ate(

%) Note:

These experiments were based on best effort platform.These experiments will be repeated on DiffServ platform when available.

Background Traffic Load

Foreground Traffic Load is 250Kbps

Foreground Traffic Loadis 3 Mbps

Electronic Visualization Laboratory University of Illinois at Chicago

Forward error correction scheme for low-latency delivery of error sensitive data

• Ray Fang, Dan Schonfeld, Rashid Ansari

• Transmit redundant data over high bandwidth networks that can be used for error correcting UDP streams to achieve lower latency than TCP.

• Transmit redundant data to improve quality of streamed video by correcting for lost packets.

Electronic Visualization Laboratory University of Illinois at Chicago

FEC Experiments

• EVL to SARA- Amsterdam (40Mb/s 200ms RT latency)

• Broader Ques:– Can FEC provide a benefit? How much?– Tradeoff between redundancy and benefit?

• Specific Ques:– TCP vs UDP vs FEC/UDP– How much jitter does FEC introduce?– High thru put UDP vs FEC/UDP to observe loss & recovery– UDP vs FEC with background traffic– FEC over QoS: WFQ or WRED congestion management-

hypothesis: WRED is bad for FEC

Electronic Visualization Laboratory University of Illinois at Chicago

UDP vs TCP vs FEC/UDP with 3:1 redundancy

  UDPLatency

(ms)

TCPLatency

(ms)

FEC over UDPLatency

(ms)128 77.0 115 90.3

256B 81.7 121 95.3

512 101.0 150.8 126.0

1024 143.0 210 189.0

2048 227.3 339 314.3

Packet size(bytes)

Electronic Visualization Laboratory University of Illinois at Chicago

Latency of transmitting 100 packets underUDP, TCP, FEC/UDP with 3:1 redundancy.

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Packet size in bytes

1-w

ay la

ten

cy in

ms

UDP

TCP

FEC over UDP

FEC greatest benefit is in small packets.

Larger packets impose greater overhead.

As redundancy decreases FEC approaches UDP.

Electronic Visualization Laboratory University of Illinois at Chicago

Packet Loss over UDP vs FEC/UDP

Data Rate(bits/s)

Packet Size(Bytes)

Packet Loss Rate in UDP (%)

Packet Loss Rate in FEC over UDP (%)

1M 128 0.4 0

1M 256 0.2 0

1M 1024 0.2 0

10M 128 30 4

10M 256 25 3

10M 1024 21 1.5

UDP

UDP

FEC

Electronic Visualization Laboratory University of Illinois at Chicago

Application Level Experiments

• Two possible candidates for instrumentation and testing over EMERGE:– Teleimmersive Data Explorer (TIDE) – Nikita

Sawant, Chris Scharver– Collaborative Image Based Rendering Viewer

(CIBR View) – Jason Leigh, Steve Lau [LBL]

Electronic Visualization Laboratory University of Illinois at Chicago

TIDE

Electronic Visualization Laboratory University of Illinois at Chicago

CIBR View

Electronic Visualization Laboratory University of Illinois at Chicago

Common Characteristics of both Teleimmersive Applications

CAVE

Im m ersaDesk

Tele-Im m ersionClients

Tele-Im m ersionServer

Rem ote Data &Com putation

Services

Compute or DatabaseQuery Spawned by

Tele-ImmersionClient and M anaged

by Tele-ImmersionServer

Electronic Visualization Laboratory University of Illinois at Chicago

• Research Goal:– Hope to see improved performance with QoS and/or TCP tuning enabled.– Monitor applications and characterize their network characteristics as it

stands over non-QoS enabled networks.– Idenitfy & remove bottlenecks in the application.– Monitor again to verify bottlenecks removed.– Monitor over QoS enabled networks.– End result is a collection of techniques and tools to help tune similar classes

of collaborative distributed applications.

• Instrumentation: Time, Info (to identify a flow), Event (to mark a special event), Inter-msg delay, 1-way latency, Read bw, Send bw, Total read, Total sent

• TIME=944767519.360357 INFO=Idesk_cray_avatar EVENT=new_avatar_entered MIN_IMD=0.000254 AVG_IMD=0.218938 MAX_IMD=1.170086 INST_IMD=0.134204 MIN_LAT=0.055527 AVG_LAT=0.169372 MAX_LAT=0.377978 INST_LAT=0.114098 AVG_RBW=74.292828 INST_RBW=750.061367 AVG_SBW=429.815557 INST_SBW=704.138274 TOTAL_READ=19019 TOTAL_SENT=110033

Electronic Visualization Laboratory University of Illinois at Chicago

Characterization of TIDE & CIBRview streams

Estimated bandwidth

(bits/s)

DiffServ

Types BurstinessLatency sensitive

Jitter sensitive

Error sensitive

UDP avatar 6K x n

(15fps)

Interactive Real-time

Constant Y Y N

UDP audio stream

64K x n Brief Y Y N

UDP video stream

10M

(2-way only)Constant Y Y YN

UDP stream

With Playback dependsNon-

interactive Real-time

Constant Y N YN

TCP control data 7K x n Reliable Brief YN YN Y

TCP bulk datadepends Best Effort

Sustained burst

N N Y

Electronic Visualization Laboratory University of Illinois at Chicago

QoSiMoto: QoS Internet Monitoring Tool

• Kyoung Park• Reads Netlogger data sets

from file or from netlogger daemon.

• CAVE application runs on SGI and Linux

• Information Visualization problem.

• How to leverage 3D.• Averaging of data points

over long traces.• www.evl.uic.edu/cavern/qosimoto