View
214
Download
0
Category
Preview:
Citation preview
Flow and Congestion Control for Reliable Multicast Communication
In Wide-Area Networks
Supratik Bhattacharyya
Department of Computer Science
University of Massachusetts Amherst
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
Flow/Congestion Control in Wide-Area Networks
Congestion Control short term : adapt
transmission rate to changing traffic conditions.
Flow Control : longer term : tailor rate
to available capacity
End-to-end approach suitable for today’s networks
Internet
Data
Data
Source
Receiver
Feedback
Feedback
Multicasting
My focus : one-to-many reliable multicasting
Network nodes replicate data packets
Network bandwidth used efficiently
Source
R1
R2R3
R4
Router
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
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
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
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
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. =
Feedback Aggregation/Filtering :Related Work
Restrict response to one LI per time interval T Montgomery 1997
Restrict response to subset of receivers :
choose K receivers 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?
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
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
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
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
Realizing the Worst Receiver Approach
Use end-to-end loss probability estimates :
N rcvrs - rcvr i reports Xi losses out of S pkts
choose rcvr with highest no. of losses
Worst Estimate-based Tracking (WET)
WET is sensitive to S : large S good estimate small S likely to choose wrong receiver as worst
Q : What can we do for small S ?
Challenge : How to identify the worst receiver?
Current Work : Robust Congestion Control
Our Idea : On LI from receiver i, reduce rate with probability
Linear Proportional Response (LPR) :
Observation : small S : LPR more robust
S : LPR allocates more than fair share to multicast session !
Example : 2 receivers, loss prob. 0.05 and 0.10
13
14
15
16
17
18
19
20
21
0 50 100 150 200 250 300 350 400 450 500
No. of observations (S)
LPR
WETFair Share
Ongoing Work
Related : Random Listening Algorithm (RLA) [Wang98]
Result : Our approach (LPR) provides tighter upper bound on r
LPR :
RLA : Nr
4 Nr 0
1
2
3
4
5
6
0 5 10 15 20 25 30
No. of receivers (N)
RLA
LPR
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
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
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
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
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
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
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
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
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
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
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
Future Research Ideas
Immediate : prototype CC protocol for
active networks robust multicast CC
schemes
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 !
Recommended