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Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks. A Doctoral Dissertation By Supratik Bhattacharyya. Talk Overview. General Problem Thesis Contributions Congestion Control for Single Multicast Group

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Flow and congestion control for reliable multicast communication in wide area networks

Flow and Congestion Control for Reliable Multicast CommunicationIn Wide-Area Networks

A Doctoral Dissertation

By

Supratik Bhattacharyya


Talk overview
Talk Overview Communication

  • General Problem

  • Thesis Contributions

  • Congestion Control for Single Multicast Group

  • Efficient Flow Control Using Multiple Multicast Groups

  • Summary and Future Research Directions


Focus of thesis

One-to-many reliable multicasting Communication

Transport-level techniques for

congestion control

flow control

Focus Of Thesis

Source

Router

R4

R1

R3

R2


Multicast flow congestion control a hard problem

Challenges - many rcvrs, many network paths : Communication

Heterogeneity

links, receiver capabilities

Scale

feedback implosion

Fairness

how to share bandwidth with unicast

Multicast Flow/Congestion Control : a hard problem

Source

R1

R3

R2

R4

: end-to-end feedback


Talk overview1
Talk Overview Communication

  • General Problem

  • Thesis Contributions

  • Congestion Control for Single Multicast Group

  • Efficient Flow Control Using Multiple Multicast Groups

  • Summary and Future Research Directions


Thesis contributions
Thesis Contributions Communication

  • Source-based Congestion Control :

    • identified and analyzed the Loss Path Multiplicity problem

    • identified a fair and scalable approach

    • formulated an axiomatic approach towards multicast congestion control

    • developed novel technique for responding to packet loss indications

    • designed a TCP-friendly protocol (NCA) for an active services architecture


Thesis contributions1
Thesis Contributions Communication

  • Flow-control:

    • developed bulk data transfer approach using multiple multicast groups.

    • proposed and evaluated algorithms for determining transmission rate of each multicast group.


Talk overview2
Talk Overview Communication

  • General Problem

  • Thesis Contributions

  • Congestion Control for Single Multicast Group

  • Efficient Flow Control Using Multiple Multicast Groups

  • Summary and Future Research Directions


Feedback aggregation

Challenge : Communication How to aggregate feedback into single rate control decision

Congestion signals (CS):

filtered versions of loss indications (LI)

 : congestion signal probability

filters can be distributed

Feedback Aggregation

congestion

signal (CS)

loss

indications (LI)

rate

change

Rate control

algorithm

filter


Problem loss path multiplicity lpm

Copies of same packet lost on Communicationmany 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

LI

LI

R3

R1

Problem : Loss Path Multiplicity (LPM)

 ?

  1 as n 

R2


How severe is the lpm problem

. . . Communication

How Severe is the LPM Problem?

Example :

end-to-end loss prob. =

p=0.05

  • Severe degradationin throughput with -

    • no. of receivers

    • independent losses

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


Feedback aggregation filtering related work
Feedback Aggregation/Filtering : CommunicationRelated Work

  • Restrict response to one LI per time interval T

    • Montgomery 1997

  • Restrict response to subset of receivers :

    • choose K rcvrs out of N asrepresentatives

    • 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?


Fair bandwidth sharing

Challenge Communication : 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.

L2

L1

“Fair” Bandwidth Sharing

Ucast1

Ucast2

Mcast

L2

L1 - 1 Mbps, L2 - 2 Mbps


Background end to end rate control algorithms
Background : End-to-end Rate Control Algorithms Communication

: rate after i-th update

  • Additive increase, multiplicative decrease :

    on congestion signal :

    else, per T :

  • We derive average session throughput B


Solution to lpm problem our approach
Solution to LPM Problem : Our Approach Communication

  • Worst Estimate-based Tracking (WET) :

    • Identify (estimate) most congested/ ”worst” receiver

    • Respond to LIs from only “worst” receiver

  • Simulations show that WET

    • prevents throttling of multicast transmission rate

    • allows fair bandwidth sharing


Architecture for loss indication based multicast congestion control

WET is one way of designing a Loss Indication Filter (LIF) Communication

Qn : Given our fairness goal, can we formulate general rules for LIF design?

Architecture for Loss Indication-based Multicast Congestion Control

loss

indications (LI)

rate

change

congestion

signal (CS)

Rate control

algorithm

filter


Axiomatic approach for loss indication filter design

N Communicationreceivers, loss probabilities

= unicast bandwidth on path to rcvr i

Axiom 1 :IfN=1, then =

Axiom 2 : If then

Axiom 3 : As

Goal : Multicast bandwidth allocation must be worst-path fair

Axiomatic Approach for Loss Indication Filter Design

. . .

2

1

N


Linear proportional response lpr
Linear Proportional Response (LPR) Communication

  • Receiver i periodically reports loss count over W packets ( estimates )

  • On LI from receiver i, source reduces rate with probability

  • Showed that LPR satisfies all three axioms


Comparison of lpr and rla

Related : Random Listening Algorithm (RLA) [Wang98] Communication

Analytic Result : LPR provides tighter upper bound on r

LPR :

RLA :

Comparison of LPR and RLA


Summary of results
Summary of Results Communication

  • LPR “more fair” than RLA for realistic W (~100 packets)

  • Steady State :

    • WET is closest to fairness goal

    • LPR is close to WET

    • RLA can be extremely unfair

  • Transient Behavior :

    • LPR, RLA respond faster to changes in network conditions than WET


Transient behavior
Transient Behavior Communication

5 mcast over all

links

  • At t=300 sec, two multicast sessions stop receiving feedback from receivers at the end of L1

L10

L1

L2

10 ucast

5 ucast

5 ucast

. . .

Loss probability on Link L2


Talk overview3
Talk Overview Communication

  • General Problem

  • Thesis Contributions

  • Congestion Control for Single Multicast Group

  • Efficient Flow Control Using Multiple Multicast Groups

  • Summary and Future Research Directions


Flow controlled bulk data transfer overview

Challenge : Communication

reliable delivery of finite volume of data

diverse receive-rates

Goal :

minimize average completiontime

Approach :

multiple IP multicast groups (channels)

Flow-controlled Bulk Data Transfer :Overview

R3=3

R1=1

R2=2

R4=4


Flow controlled bulk data transfer

Q : Communication 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

Flow-controlled Bulk Data Transfer

2 pkts/sec

4 pkts/sec

1 pkt/sec

R2

a

R4

R1

b

a

a

b

c

d

R1,R2,R4

r1 = 1

a

b

d

c

r2 = 1

d

b

R2,R4

c

d

r3 = 2

R4


Flow controlled bulk data transfer1

Q : Communication 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

d

Flow-controlled Bulk Data Transfer

2 pkts/sec

4 pkts/sec

1 pkt/sec

R2

a

R4

R1

b

a

a

c

b

c

d

R1,R2,R4

r1 = 1

a

b

d

c

r2 = 1

d

b

R2,R4

c

d

r3 = 2

R4


Flow controlled bulk data transfer2

Q : Communication How to :

assign channel rates?

assign receivers to channels?

partition data among channels?

Assumptions :

error-free channels

known, static receive-rate constraints

Solution withunlimited channels :

minimizes average completion time

minimizes bandwidth

c

d

Flow-controlled Bulk Data Transfer

2 pkts/sec

4 pkts/sec

1 pkt/sec

R2

a

R4

R1

b

a

b

a

c

b

d

c

d

R1,R2,R4

r1 = 1

a

b

d

c

r2 = 1

d

b

R2,R4

c

d

r3 = 2

R4


Summary of results1
Summary of Results Communication

  • Developed solution for minimizing average completion time with N receivers and K channels

  • Developed simple rate assignment algorithms that

    • scale well to large number of receivers

    • have close to optimal average completion time

    • make efficient use of network bandwidth

  • Showed that small number of multicast groups sufficient for above algorithms


Summary of contributions
Summary of Contributions Communication

  • Source-based Congestion Control :

    • identified and analyzed the Loss Path Multiplicity problem

    • identified a fair and scalable approach

    • formulated an axiomatic approach towards multicast congestion control

    • developed novel technique for responding to packet loss indications

    • designed a TCP-friendly protocol (NCA) for an active services architecture


Summary of contributions1
Summary of Contributions Communication

  • Flow-control:

    • developed bulk data transfer approach using multiple multicast groups.

    • proposed and evaluated algorithms for determining transmission rate of each multicast group.


Future research directions congestion control
Future Research Directions : Congestion Control Communication

  • WET :

    • How can the source detect changes in network congestion levels in a timely fashion?

  • LPR :

    • Can steady state performance be improved?

    • Can the NCA protocol be based on LPR instead of WET?

  • NCA :

    • implementation details - start-up, nominee changeover, etc.


Future research directions flow control
Future Research Directions : CommunicationFlow Control

  • Flow-controlled bulk data transfer :

    • evaluate performance when sender has imperfect knowledge of receive-rates

    • explore feasibility of our approach in a practical setting

    • Synergy with per-group congestion control techniques


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