1 Flow and Congestion Control for Reliable Multicast Communication In Wide-Area Networks A Doctoral Dissertation By Supratik Bhattacharyya

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

3 Focus Of Thesis  One-to-many reliable multicasting  Transport-level techniques for congestion control flow control Source R1 R2 R3 R4 Router

4 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

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

6 Thesis Contributions  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

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

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

9 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

10 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  

11 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. =

12 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  Reduce response to each LI : Golestani, Bhattacharyya 1998, Delucia et al Q : How much bandwidth should a multicast session get?

13 “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

14 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

15 Solution to LPM Problem : Our Approach  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

16 Architecture for Loss Indication-based Multicast Congestion Control loss indications (LI) filter Rate control Rate controlalgorithm congestion signal (CS) rate change  WET is one way of designing a Loss Indication Filter (LIF)  Qn : Given our fairness goal, can we formulate general rules for LIF design?

17 Axiomatic Approach for Loss Indication Filter Design N receivers, loss probabilities = unicast bandwidth on path to rcvr i Axiom 1 : If N=1, then = Axiom 2 : If then Axiom 3 : As Goal : Multicast bandwidth allocation must be worst-path fair 1 2 N...

18 Linear Proportional Response (LPR)  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

19 Comparison of LPR and RLA  Related : Random Listening Algorithm (RLA) [Wang98]  Analytic Result : LPR provides tighter upper bound on r LPR : RLA :

20 Summary of Results  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

21 Transient Behavior  At t=300 sec, two multicast sessions stop receiving feedback from receivers at the end of L ucast 5 ucast L1 L2 5 mcast over all links L10 Loss probability on Link L2

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

23 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 R 4 =4

24 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

25 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

26 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

27 Summary of Results  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

28 Summary of Contributions  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

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

30 Future Research Directions : Congestion Control  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.

31 Future Research Directions : Flow 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