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Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks

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Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks

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Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks

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

A Doctoral Dissertation

By

Supratik Bhattacharyya

- General Problem
- Thesis Contributions
- Congestion Control for Single Multicast Group
- Efficient Flow Control Using Multiple Multicast Groups
- Summary and Future Research Directions

One-to-many reliable multicasting

Transport-level techniques for

congestion control

flow control

Source

Router

R4

R1

R3

R2

Challenges - many rcvrs, many network paths :

Heterogeneity

links, receiver capabilities

Scale

feedback implosion

Fairness

how to share bandwidth with unicast

Source

R1

R3

R2

R4

: end-to-end feedback

- General Problem
- Thesis Contributions
- Congestion Control for Single Multicast Group
- Efficient Flow Control Using Multiple Multicast Groups
- Summary and Future Research Directions

- 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

- Flow-control:
- developed bulk data transfer approach using multiple multicast groups.
- proposed and evaluated algorithms for determining transmission rate of each multicast group.

- General Problem
- Thesis Contributions
- Congestion Control for Single Multicast Group
- Efficient Flow Control Using Multiple Multicast Groups
- Summary and Future Research Directions

Challenge : 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

congestion

signal (CS)

loss

indications (LI)

rate

change

Rate control

algorithm

filter

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

LI

LI

R3

R1

?

1 as n

R2

. . .

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

- 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?

- Golestani, Bhattacharyya 1998, Delucia et al. 1997

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.

L2

L1

Ucast1

Ucast2

Mcast

L2

L1 - 1 Mbps, L2 - 2 Mbps

: rate after i-th update

- Additive increase, multiplicative decrease :
on congestion signal :

else, per T :

- We derive average session throughput B

- 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

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

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

loss

indications (LI)

rate

change

congestion

signal (CS)

Rate control

algorithm

filter

N receivers, 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

. . .

2

1

N

- 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

Related : Random Listening Algorithm (RLA) [Wang98]

Analytic Result : LPR provides tighter upper bound on r

LPR :

RLA :

- 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

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

- General Problem
- Thesis Contributions
- Congestion Control for Single Multicast Group
- Efficient Flow Control Using Multiple Multicast Groups
- Summary and Future Research Directions

Challenge :

reliable delivery of finite volume of data

diverse receive-rates

Goal :

minimize average completiontime

Approach :

multiple IP multicast groups (channels)

R3=3

R1=1

R2=2

R4=4

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

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

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

d

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

Q : 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

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

- 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

- 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

- Flow-control:
- developed bulk data transfer approach using multiple multicast groups.
- proposed and evaluated algorithms for determining transmission rate of each multicast group.

- 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.

- 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