1. 1 Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks Supratik Bhattacharyya Department of Computer Science University of Massachusetts Amherst

2. 2 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

3. 3 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 R4 R3 R2

4. 4 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

5. 5 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. 6 Feedback Aggregation Challenge : How to aggregate feedback into single rate control decision loss indications (LI) filter Rate control Rate controlalgorithm congestion signal (CS) rate change  Congestion signals (CS): filtered versions of loss indications (LI) : congestion signal probability filters can be distributed

7. 7 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 R2 ? R1 R3 LI  1 as n  

8. 8 How Severe is the LPM Problem?  Severe degradation in throughput with - no. of receivers independent losses p=0.05 Example : f : fraction of end-to-end loss on independent link... end-to-end loss prob. =

9. 9 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?

10. 10 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 Ucast 1 L2 L1 Mcast Ucast 2 L2

11. 11 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

12. 12 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

13. 13 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 ucast over L1 ucast over 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

14. 14 Realizing the Worst Receiver Approach  Use end-to-end loss probability estimates : K rcvrs - rcvr i reports X i 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?

15. 15 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

16. 16 Ongoing Work  Related : Random Listening Algorithm (RLA) [Wang98]  Result : Our approach (LPR) provides tighter upper bound on r LPR : RLA :

17. 17 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. estimate RTT : round trip time estimate Source R1 R2 R3 R4 AS1 AS2 Our Rate Control Algorithm v1 v2 v3 v4 v1 v4 Worst : R1

18. 18 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

19. 19 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) R 1 =1R 2 =2 R 3 =3 Bhattacharyya, Kurose, Towsley, Nagarajan. Infocom ‘98 R 4 =4

20. 20 Flow-controlled Bulk Data Transfer 2 pkts/sec 4 pkts/sec 1 pkt/sec a b c d bd r 1 = 1 r 2 = 1 r 3 = 2 c d R1 R2 R4 a a a b b c d 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

21. 21 Flow-controlled Bulk Data Transfer 2 pkts/sec 4 pkts/sec 1 pkt/sec a b c d bd r 1 = 1 r 2 = 1 r 3 = 2 c d R1 R2 R4 a a a b b c d 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 c d

22. 22 Flow-controlled Bulk Data Transfer 2 pkts/sec 4 pkts/sec 1 pkt/sec a b c d bd r 1 = 1 r 2 = 1 r 3 = 2 c d R1 R2 R4 a a a b b c d 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 c d d b

23. 23 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(N 3 K)  Dynamic rate assignment : reassign rates when faster receivers finish optimization problem too hard Our approach : Simple heuristics

24. 24 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: G1 G2 G3 G4

25. 25 Summary of Results  Average Completion time scales well :  Small no. of channels reqd :

26. 26 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

27. 27 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

28. 28 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 !