1. Dynamic Behavior of Slowly Responsive Congestion Control Algorithms (Bansal, Balakrishnan, Floyd & Shenker, 2001)

2. TCP Congestion Control Largely responsible for the stability of the internet in the face of rapid growth Implemented cooperatively by end hosts (not enforced by routers) Works well because most traffic is TCP

3. TCP vs Real-Time Streaming TCP congestion control aggresively modulates bandwidth “Real-time” streaming applications want smooth bandwidth changes Alternative protocols may break the internet What about a TCP-compatible protocol?

4. TCP Compatibility A compatible protocol sends data at roughly the same rate as TCP in the face of the same amount of packet loss This rate is measured at the steady state, when it has stabilized against a fixed packet loss rate In practice the packet loss rate is highly dynamic So are compatible protocols safe in practice?

5. Some Terms TCP-equivalent: rate based on AIMD with the same parameters (a=1, b=2) TCP-compatible: over several RTTs, rate is close to TCP for any given packet loss rate TCP-compatible protocols are slowly responsive if their rate changes less rapidly than TCP in response to a change in the packet loss rate

6. 4.5 alternatives TCP(b): shrink window by (1/b) after packet loss (b=2 in standard TCP) SQRT(b): Binomial algorithm, gentler version of TCP: adjust window w to (1-1/b)*w 1/2 RAP(b): equation-based TCP equivalent TFRC(b): Adjust rate based on an exponentially weighted moving average over b loss events (propose b=6) TFRC(b) with self-clocking: following a packet loss, limit sending rate to receiver’s rate of reception during previous round trip (default allows 2x sending rate). Less strict limit in absence of loss.

7. Metrics Smoothness: largest ratio of new rate to old rate over consecutive round trips (1 is perfect) Fairness(d): round trips until two flows come within d% of equal bandwidth, when one starts with 1 packet/RT (d=10) f(k): fraction of bandwidth used after k round trips when available bandwidth has doubled Stabilization time: time until sending rate is within 1.5 times steady state value at the new packet loss rate Stabilization cost: Average packet loss rate * stabilization time

8. Simulations Flows pass through single bottleneck router using RED queue management (packet drop rate is proportional to queue length) Square-wave competing flow Flash mobs of short-lived HTTP requests

9. Stabilization cost Vertical axis is logarithmic: worst case cost is two orders of magnitude higher than TCP. Oh no! But proposed algorithm parameters are from 2-6: worst case is maybe 3x? Not nearly as scary. b=256 is much more expensive, not appreciably smoother

10. Stabilization time Remember, proposed algorithm parameters are from 2-6. Large numbers included to demonstrate theoretical value of self-clocking?

11. Flash mob response In all three cases, total throughput appears close to total bandwidth Again, a demonstration of self-clocking. Where is TFRC(6)? Is TFRC-SC(256) too fair? Streaming applications might prefer the middle path This test is one TFRC flow vs 1000 HTTP flows. What happens if there are 10, or 100?

12. Long-term Fairness x-axis: square-wave period, competing flow consumes 2/3 available bandwidth Link utilization is poor when square-wave period is.2 seconds TCP gets more newly- available bandwidth than TFRC when period is from.4 to about 10 seconds TCP never loses

13. Transient Fairness TCP(b) is inverted in these graphs: W’ = (1-b)W TCP takes a long time to converge when b is low TFRC takes a long time when b is high Neither takes very long when b is reasonable TFRC convergence time is less than linear with b

14. Aggressiveness f(k) is percent of available bandwidth used after k round trips, when bandwidth has doubled As expected, slowly- responsive protocols take much longer to take advantage of an increase in bandwidth

15. Effectiveness TFRC=TFRC(6) When bandwidth oscillates over a 3:1 range, overall link utilization is comparable across protocols Over a 10:1 range, TFRC can perform quite badly Performance is good when burst period is within RTT history

16. Smoothness Best case (steady packet loss rate): TFRC is perfectly smooth, TCP exhibits sawtooth pattern TFRC performs well under mildly bursty conditions (3 rapid loss events followed by 3 slow = 6 events, fits within TFRC(6) history) In this rosy scenario, TFRC is not only smoother but gets better throughput

17. Awkwardness It is possible to find a burst pattern for any TFRC parameter that results in very bad perfomance - poor utilization and no smoothness: just string together enough loss events to fill TFRC history window, then suddenly remove congestion How likely is this to occur naturally? Not sure.

18. Conclusions All of the proposed TCP-compatible alternatives appear safe (assuming this paper’s traffic model is reasonable). If anything, they may be too fair. Self-clocking is a useful fail-safe slowly-responsive protocols usually provide smooth bandwidth changes but not always

19. Open questions All traffic models were periodic with a fixed small number of flows. What happens when the number of flows varies? Most tests relied on RED queue management. How would they look under drop-tail (which is currently prevalent)? How does self-clocking affect TFRC smoothness (given that smoothness is TFRC’s principal advantage)? Is smoothness at the granularity of a round trip particularly useful?