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Flow and Congestion Control for Reliable Multicast Communication

In Wide-Area Networks

Supratik Bhattacharyya

Department of Computer Science

University of Massachusetts Amherst

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Talk Overview

General Problem

Single-rate source-based congestion control (CC) :

the Loss Path Multiplicity problem

a scalable and “fair” congestion control approach

a prototype implementation for active networks

Multi-rate flow-controlled bulk data transfer

Future Research Ideas

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Multicast Flow/Congestion Control : a hard problem

Challenges - many rcvrs, many network paths :

Heterogeneity

– links, receiver capabilities

Scale– feedback implosion

Fairness – how to share bandwidth

with unicast: end-to-end feedback

Source

R1 R4R3R2

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Multicast Flow/Congestion Control : Basic Approaches

Source-controlled : source sets rate rate may be constrained by

slowest receiver suited for reliable, delay-

tolerant apps

Receiver-controlled : data layered and sent on

many IP multicast groups

dynamic leave/joins by receivers

suited for audio/video

r r1

r2 r2

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Talk Overview

General Problem

Single-rate source-based congestion control (CC) :

the Loss Path Multiplicity problem

a scalable and “fair” congestion control approach

a prototype implementation for active networks

Multi-rate flow-controlled bulk data transfer

Future Research Ideas

6

Feedback Aggregation

Challenge : How to aggregate feedback into single rate control decision

loss indications (LI)

filterfilter Rate controlRate control

algorithmalgorithm

congestion signal (CS)

rate change

Congestion signals (CS): filtered versions of loss indications (LI) : congestion signal probability filters can be distributed

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Problem : Loss Path Multiplicity (LPM)

Copies of same packet lost on many network paths

Set of receivers treated as single aggregate receiver

Example :

n : no. of receivers

p : loss prob. on link to each rcvr.

: congestion signal probability

) 1 (1 p n

R2

?

R1 R3

LI LI

1 as n

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How Severe is the LPM Problem?

Severe degradation in throughput with -

no. of receivers independent losses

0

2

4

6

8

10

12

0 50 100150200 250300350400450 500

No. of Receivers

f=0.1

f=0.5

f=0.9

p=0.05

Example :

p

f : fraction of end-to-end loss on independent link

. . .

fpend-to-end loss prob. =

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Feedback Aggregation/Filtering :Related Work

Restrict response to one LI per time interval T Montgomery 1997

Restrict response to subset of receivers :

choose K rcvrs out of N as representatives

Delucia et al. 1997

Reduce response to each LI :

Golestani, Bhattacharyya 1998, Delucia et al. 1997

Q : How much bandwidth should a multicast session get?

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Background : “Fair” Bandwidth Sharing

Challenge : How to achieve “fair” sharing among multicast and unicast sessions

Multicast allocation according to “worst” end-to-end path

Multicast session shares equally with a unicast session on its “worst” end-to-end path.

L1 - 1 Mbps, L2 - 2 Mbps

Ucast1

L2

L1

Mcast

Mbpsr 5.0 1Ucast

Mbpsr 5.0 Mcast

Mbpsr 5.1 Ucast2

Ucast2

L2

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Background : End-to-end Rate Control Algorithms

: rate after i-th update

Additive increase, multiplicative decrease :

on congestion signal :

else, per T :

We derive average session throughput B

1 1 rr ii

)11( 1 Crr ii

ri

, ,TCCTB

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Solution to LPM Problem : Our Approach

Identify (estimate) “worst” receiver

Respond to LIs from only “worst” receiver

prevents throttling of multicast transmission rate

allows fair bandwidth sharing

Bhattacharyya, Towsley, Kurose. Infocom ‘99

. . .

Modified Star

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25 30 35 40 45 50

No. of Representatives (K)

representativeapproach

worst rcvr. approach

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Simulation of LPM Solution

Simulation Settings: 5 multicasts over L1, L2, each

tracks L1 A : 5 unicasts over L1, 5 over L2 B : 5 more unicasts on L1 C : same as B, each multicast

tracks L2 instead

Example topology :

L1 L2

L1, L2 : 300 pkts/sec

Sources

Rcvrs

mcast ucastover L1

ucastover L2

Simulation Settings

A

B

C

29.8 30.2 30.3

Throughput (pkts/sec)

20.9

30.0

20.9 39.9

17.1 30.5

Rcvrs

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Realizing the Worst Receiver Approach

Use end-to-end loss probability estimates :

K rcvrs - rcvr i reports Xi losses out of N pkts

choose rcvr with highest no. of losses

Worst Estimate-based Tracking (WET)

WET is sensitive to N : large N good estimate small N likely to choose wrong receiver as worst

Q : What can we do for small N ?

Challenge : How to identify the worst receiver?

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Current Work : Robust Filters

Our Idea : On LI from receiver i, reduce rate with probability

Linear Proportional Response (LPR) :

Observation : small N : LPR more robust

N : LPR allocates more than fair share to multicast session !

Example : 2 receivers, loss prob. 0.05 and 0.10

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14

15

16

17

18

19

20

21

0 50 100 150 200 250 300 350 400 450 500

No. of observations (N)

LPR

WETFair Share

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Ongoing Work

Related : Random Listening Algorithm (RLA) [Wang98]

Result : Our approach (LPR) provides tighter upper bound on r

LPR :

RLA : Kr

4 Kr 0

1

2

3

4

5

6

0 5 10 15 20 25 30

No. of receivers (K)

RLA

LPR

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A Prototype of Worst Receiver Approach for Active Networks

“Worst” receiver has largest value of

Active Servers : aggregate feedback

help in identifying “worst” receiver

p : loss prob. estimateRTT : round trip time estimate

Source

R1 R2 R3 R4

AS1 AS2

Our Rate Control Algorithm

pRTTv

v1 v2v3 v4

v1 v4

Worst : R1

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Talk Overview

General Problem

Single-rate source-based congestion control (CC) :

the Loss Path Multiplicity problem

a scalable and “fair” congestion control approach

a prototype implementation for active networks

Multi-rate flow-controlled bulk data transfer

Future Research Ideas

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Flow-controlled Bulk Data Transfer : Overview

Challenge : reliable delivery of finite volume

of data diverse receive-rates

Goal : minimize average completion

time

Approach : multiple IP multicast groups

(channels)

R1=1 R2=2 R3=3

Bhattacharyya, Kurose, Towsley, Nagarajan. Infocom ‘98

R4=4

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Flow-controlled Bulk Data Transfer

2 pkts/sec4 pkts/sec

1 pkt/sec

a

b

c

d

b dr1 = 1

r2 = 1

r3 = 2 c d

R1R2

R4

a

a

a

b

b

cd

R1,R2,R4

R2,R4

R4

Q : How to : assign channel rates? assign receivers to channels? partition data among

channels?

Assumptions : error-free channels known, static receive-rate

constraints

Solution with unlimited channels :

minimizes average completion time

minimizes bandwidth

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Flow-controlled Bulk Data Transfer

2 pkts/sec4 pkts/sec

1 pkt/sec

a

b

c

d

b dr1 = 1

r2 = 1

r3 = 2 c d

R1

R2R4

a

a

a

b

b

cd

R1,R2,R4

R2,R4

R4

Q : How to : assign channel rates? assign receivers to channels? partition data among

channels?

Assumptions : error-free channels known, static receive-rate

constraints

Solution with unlimited channels :

minimizes average completion time

minimizes bandwidth

c

cd

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Flow-controlled Bulk Data Transfer

2 pkts/sec4 pkts/sec

1 pkt/sec

a

b

c

d

b dr1 = 1

r2 = 1

r3 = 2 c d

R1

R2R4

a

a

a

b

b

cd

R1,R2,R4

R2,R4

R4

Q : How to : assign channel rates? assign receivers to channels? partition data among

channels?

Assumptions : error-free channels known, static receive-rate

constraints

Solution with unlimited channels :

minimizes average completion time

minimizes bandwidth

c

cd

d

b

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Limited Number of Channels

Static rate assignment :

Q : Given K channels and N (>K) receive rates, which K rates to match?

Approach : minimize average completion time

dynamic programming solution - O(N3 K)

Dynamic rate assignment : reassign rates when faster receivers finish optimization problem too hard Our approach : Simple heuristics

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Heuristics for Channel Rate Assignment

Fastest Receivers First (FRF)

Slowest Receivers First (SRF)

Equal Partitions (EQ) distribute rates “smoothly” over entire

range of receive rates

Maximize Utilized Capacity (MUC) :

allocate channel rate to maximize sum of rates at which unfinished receivers receive

dynamic programming solution

no. of receivers

receive rates

Example :

Choose rates for 3 channels

EQ:

MUC:

G1G2

G3

G4

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Summary of Results

Average Completion time scales well :

200

1000

1500

0 100 200 300 400

No. of Receivers (X)

SRF

STATIC

MUC

IDEAL

Small no. of channels reqd :

200

1000

2600

0 2 4 6 8 10 12 14 16

Number of Channels (K)

SRF

STATICMUC

IDEAL

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Summary of Contributions

Single-rate source-oriented multicast CC : identified and studied Loss Path Multiplicity

problem proposed a scalable and “fair” congestion control

approach current work : robust congestion control schemes developing a prototype implementation for active

networks

Developed efficient algorithms for flow-controlled multicast of bulk data 1

1 : U.S. patent pending

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Other Interesting Projects

RMTP : A Reliable Multicast Transport Protocol 1

A Class of End-to-end Congestion Control Algorithm for the Internet 2

Design and Implementation an Adaptive Data Link Layer Protocol for an ATM Wireless LAN

2 : Golestani and Bhattacharyya. ICNP ‘98

1 : Paul, Sabnani, Lin, Bhattacharyya. JSAC 97

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Future Research Ideas

Immediate : CC protocol for active

networks robust multicast CC

approaches

Short Term : multicast CC for continuous

media CC with enhanced network

support

Looking ahead :

network measurements support for adaptive

applications active services differentiated services

Open to new ideas and collaborations !