1. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-1 Source-Based Multicast Congestion Control P. Thapliyal, Sidhartha, S.Kalyanaraman Rensselaer Polytechnic Institute Contact: shivkuma@ecse.rpi.edu http://www.ecse.rpi.edu/Homepages/shivkuma

2. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-2 q A case for purely source-based CC. q Scheme: q Concepts q 3-Stage Filters q RTT estimation issues q Sample performance results q Drop-to-zero resistance q TCP-friendliness Overview

3. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-3 Pure Source-based CC q Leverages existing RMT reverse traffic (NAKs, Bitmaps) q Very low (or zero) control traffic requirements q Implemented at source => no other support required in receivers, network elements, aggregators and packet headers. q Deployment simplicity q Weak, but minimal requirements on RTT estimation q Low (*, G) state/computational requirements at the sender q Could extend relatively easily to multi-sender case (future) q Sender-based MCC: Cons: q Minimal reverse control traffic carrying implicit congestion and timing information required

4. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-4 Concepts q Unicast: q During congestion, receiver sends multiple loss indications (LIs) per RTT q Sender responds to at most one LI per RTT: “Loss Event” (LEs) or “Congestion Indication” (CI) q Multicast: LI i available per-receiver => Total LIs =  i Li i q Drop-to-zero problem without filtering => 3-stage filters. Stage 1: LI2LE filtering on a per-receiver basis (LI i  LE i ) q Total LEs after LI2LE filtering =  i LE i Stage 2: Pass Max i {LE i } out of  i LE i Stage 3: Pass at most one filtered LE (I.e. CI) per RTT

5. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-5 q Stage 1: LI2LE (Loss Indication  Loss Event) filter q Stage 2: Max-LPRF (Linear Proportional Response Filter) q Stage 3: ATF: Adaptive Time Filter q Rate-based AIMD (Additive Increase Multiplicative Decrease) q RTT Estimator 3-Stage Filter Model RTT Estimator AIMD LI 2 LE Max- LPRF Loss Indications (Lis) ATF Loss event (LEs) Max{LEs} Congestion Indications (CIs) Estimated RTT

6. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-6 q Goal: Per receiver, pass at most one LI per RTT. q These filtered LIs are considered to be LEs q Per receiver timestamp (T i ) noted when the last LI passed q LI i is passed if current time (t) - T i > k*RTT q k = 1 typically Per-receiver Timestamp LI2LE LIs LEs RTT estimate LI2LE Filter

7. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-7 Max-LPRF (Linear Proportional Response Filter) q Filters every LE with the probability: q Xi: the number of loss events received from receiver i. q Max{Xi}: the Max LE count from any one receiver. q  Xi: the total number of LEs received from all receivers. Per receiver LE counter Max- LPRF  LE i Max i {X i } Decay function RTT estimate XiXi

8. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-8 Adaptive Time Filter (ATF) Concepts q Assume tree with just a single multicast flow q Congestion can occur independently in branches due to capacity changes, buffer sizing etc q Congestion “epoch”: Sub-period of congestion where: q the source reduces its rate exactly once and q gives enough time for rate-reduction to diffuse through the congested sub-tree. q To avoid drop-to-zero (due to independent congestion events):  The number of congestion epochs should be exactly = ceil {log 2 ( /  min )} q  min is the minimum available rate for the RMT flow anywhere in the tree; is the source rate before congestion q Ideally,congestion epoch=largest RTT of congested sub-tree

9. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-9 Timelines: Total Congestion Period = RTT1 + RTT2 Congestion Epoch 1 = RTT1 Congestion Epoch 2 = RTT2 Congestion Epochs, Congested Sub-tree Structures, Longest RTT, Total Congestion Period

10. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-10 RTT Estimation Issues q Sampling: q Local timestamp recorded when packet transmitted q RTT sample when NAK is received q Ideally, RTT samples not taken for NAKs which are re- transmitted (or NAKs which correspond to RDATA losses) q Statistics collection: q Reject 90% of samples smaller than SRTT/2 (SRTT =avg) q Use most samples from highest “order of magnitude” RTT q Exponentially average the samples (SRTT) and the deviations (|sample - SRTT|) q Note: this does not guarantee getting samples from largest RTT sub-tree if there is aggregation. q If samples come from different sub-trees, it will be reflected in the smoothed mean deviation

11. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-11

12. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-12 RTT Estimation and AIMD q Reject 90% of samples smaller than SRTT/2 q Use most samples from highest “order of magnitude” RTT q Canonical congestion epoch = SRTT + 4D (like TCP timeout) q Accounts for variance in remaining samples q NAKs filtered out till end of congestion epoch q Assume rexmitted NAKs not included in RTT estimation q No rate increases during epoch q Additional silence period of SRTT/2 q No source traffic sent after rate reduction for SRTT/2 q Allows collection of NAKs from newly congested sub-tree q Compensates for partial or no aggregation q Only “new” NAKs can trigger rate decrease (and fresh epochs) q Rate increase interval = SRTT + 2D (empirically set)

13. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-13 Effect of Multiplexing, Fanout RM receivers 6 Mbps 5 Mbps Main source Set 1 Set 2 Set 3 Set 4 Destination 1 Destination 2 Destination 3 Destination 4

14. Shivkumar Kalyanaraman Rensselaer Polytechnic Institute 1-14 Summary q Pure source-based MCC could potentially have zero control traffic requirements and reduced complexity q Needs congestion indications and implicit timing information from underlying feedback stream q Idea: emulate unicast model q 3-stage filters: LI2LE, Max-LPRF, ATF q AIMD and RTT estimation Module q Preliminary results (assuming un-aggregated LIs): q Drop-to-zero resistance and TCP friendliness