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

Explore single-rate source-based congestion control, scalable approaches, and fair sharing strategies in wide-area multicast communication. Understand challenges, solutions, and future research ideas for efficient flow and congestion management.

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

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

  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. 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 Flow/Congestion Control in Wide-Area Networks Source Data Feedback Internet Data Feedback Receiver

  4. My focus : one-to-many reliable multicasting Network nodes replicate data packets Network bandwidth used efficiently Multicasting Source Router R4 R1 R3 R2

  5. Challenges - many rcvrs, many network paths : 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

  6. 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

  7. 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 Feedback Aggregation congestion signal (CS) loss indications (LI) rate change Rate control algorithm filter 

  8. 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 Problem : Loss Path Multiplicity (LPM)  ?   1 as N  R2

  9. . . . 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

  10. 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 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?

  11. 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 Background : “Fair” Bandwidth Sharing Ucast1 Ucast2 Mcast L2 L1 - 1 Mbps, L2 - 2 Mbps

  12. 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

  13. . . . Solution to LPM Problem : Our Approach Modified Star • 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

  14. 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 : Simulation of LPM Solution Sources L1 L2 Throughput (pkts/sec) Simulation Settings ucast over L2 ucast over L1 mcast A 29.8 30.2 30.3 Rcvrs Rcvrs B 39.9 20.9 20.9 C 30.0 17.1 30.5 L1, L2 : 300 pkts/sec

  15. Use end-to-end loss probability estimates : N rcvrs - rcvri reportsXilosses out ofS 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? Realizing the Worst Receiver Approach

  16. 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 ! Current Work : Robust Congestion Control Example : 2 receivers, loss prob. 0.05 and 0.10

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

  18. “Worst” receiver has largest value of Active Servers : aggregate feedback help in identifying “worst” receiver A Prototype of Worst Receiver Approach for Active Networks Source Our Rate Control Algorithm Worst : R1 v1 v4 AS2 AS1 v1 v2 v4 v3 R2 R1 R4 R3 p :loss prob. estimate RTT : round trip time estimate

  19. 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

  20. Challenge : 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 Bhattacharyya, Kurose, Towsley, Nagarajan. Infocom ‘98

  21. 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/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

  22. 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 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

  23. 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 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

  24. 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

  25. Heuristics for Channel Rate Assignment Example : Choose rates for 3 channels • 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 sumof rates at which unfinished receivers receive • dynamic programming solution no. of receivers G3 G2 G1 G4 receive rates EQ: MUC:

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

  27. 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

  28. 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 1 : Paul, Sabnani, Lin, Bhattacharyya. JSAC 97 2 : Golestani and Bhattacharyya. ICNP ‘98

  29. 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 ! Future Research Ideas

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