Receiver-driven Layered Multicast

1. Receiver-driven Layered Multicast Paper by- Steven McCanne, Van Jacobson and Martin Vetterli – ACM SIGCOMM 1996 Presented By – Manoj Sivakumar

2. Overview • Introduction • Approaches to Rate-Adaptive Multimedia • Issues and challenges • RLM - Details • Performance Evaluation • Conclusions

3. Introduction • Consider a typical streaming Application • What rate should the source send data at ? Receiver Source Internet 128 Kb/s X Kb/s

4. Approaches to Rate-Adaptive Multimedia • Rate Adaptation at Source – based on available network capacity • Works well for a Unicast environment • How about multicast ? Receiver 1 X1 Kb/s source Receiver 2 128 Kb/s X2 Kb/s Receiver 3 X3 Kb/s

5. Example of Heterogeneity

6. Issues and Challenges • Optimal link utilization • Best possible service to all receivers • Ability to cope with Congestion in the network • All this should be done with just best effort service on the internet

7. Layered Approach • Rather than sending a single encoded video signal the source sends several layers of encoded signal – each layer incrementally refining the quality of the signal • Intermediate Routers drop higher layers when congestion occurs

8. Layered Approach • Each layer is sent to one multicast group • If a receiver wants higher quality – subscribes to all higher level layer multicast groups

9. Issue in Layered Approach • No framework for explicit signaling between the receivers and routers • A mechanism to adapt to both static heterogeneity and dynamic variations in network capacity is not present • Solution - RLM

10. RLM – Network Model • Works with IP Multicast • Assume • Best effort (packets may be out of order, lost or arbitrarily delayed) • Multicast (traffic flows only along links with downstream recipients) • Group oriented communication (senders do not know of receivers and receivers can come and go) • Receivers may specify different senders

11. RLM - Video Streams • One channel per layer • Layers are additive • Adding more channels gives better quality • Adding more channels requires more bandwidth

12. RLM Sessions • Each session composed of layers, with one layer per group • Layers can be separate (i.e. each layer is higher quality) or additive (add all to get maximum quality) • Additive is more efficient

13. Router Mechanisms • Dropping of packets • Drop less preferential packets first

14. RLM - Protocol • Abstraction • on congestion, drop a layer • on spare capacity, add a layer

15. RLM – Adding and Dropping layers • Drop layer when packet loss • Add does not have counter-part signal • Need to try adding at well-chosen times • Called join experiment

16. RLM – Adding and Dropping layers • If join experiment fails • Drop layer, since causing congestion • If join experiment succeeds • One step closer to operating level • But join experiments can cause congestion • Only want to try when might succeed

17. RLM – Join Experiments • Get lowest layer and start timer for next probe • Initially timer small • If higher level fails then increase timer duration else proceed to next layer and start time for the layer above it • Repeat until optimum

18. RLM Join Experiment • How to know is join experiment succeeded • Detection time

19. Detection Time • Hard to estimate • Can only be done experimentally • Initially start with a large value • Progressively update the detection time based on actual values

20. RLM - Issues with Joins • Is this Scalable • What if each node does join experiments and the same time for different layers • Wrong info to node that requests lower layer if the other node had requested higher layer • Solution – Shared Learning

21. RLM – Shared Learning • Each node broadcasts its intent to the group • Adv’s – other nodes can learn from the result of this node’s experiment • Reduction in simultaneous experiments • Is this still foolproof ??

22. RLM - Evaluation • Simulations performed in NS • Video modeled as CBR • Parameters • Bandwidth: 1.5 Mbps • Layers: 6, each 32 x 2m kbps (m = 0 … 5) • Queue management :Drop Tail • Queue Size (20 packets) • Packet size (1 Kbytes) • Latency (varies) • Topology (next slide)

23. RLM - Evaluation • Topologies • 1 – explore latency • 2 – explore scalability • 3 – heterogeneous with two sets • 4 – large number of independent sessions

24. RLM – Performance Metrics • Worse-case lost rate over varying time intervals • Short-term: how bad transient congestion is • Long-term: how often congestion occurs • Throughput as percent of available • But will always be 100% eventually • So, look at time to reach optimal • Note, neither alone is ok • Could have low loss, low throughput • High loss, high throughput • Need to look at both

25. RLM – Performance Results • Latency Results

26. RLM – Performance Results • Latency Results

27. RLM – Performance Results • Session Size

28. RLM – Performance Results • Convergence rate

29. RLM – Performance Results • Bandwidth Heterogeneity

30. Conclusions • Possible Pitfalls • Shared Learning assumes only multicast traffic • Is this valid ?? • Is congestion produced by Multicast traffic alone • Simulation does not other traffic requests!!

31. Conclusions • Overall – a nice architecture and mechanism to regulate traffic and have the best utilization • But still needs refinement

32. References • S. McCanne, V. Jacobson, and M. Vetterli, "Receiver-driven layered multicast," in Proc. SIGCOMM'96, ACM, Stanford, CA, Aug. 1996, pp. 117--130.