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Transcript
Receiver-driven Layered Multicast Paper by- Steven McCanne, Van Jacobson and Martin Vetterli – ACM SIGCOMM 1996 Presented By – Manoj Sivakumar Overview Introduction Approaches to Rate-Adaptive Multimedia Issues and challenges RLM - Details Performance Evaluation Conclusions Introduction Consider a typical streaming Application Source 128 Kb/s Internet X Kb/s Receiver What rate should the source send data at ? 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 128 Kb/s X2 Kb/s X3 Kb/s Receiver 2 Receiver 3 Example of Heterogeneity 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 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 Layered Approach Each layer is sent to one multicast group If a receiver wants higher quality – subscribes to all higher level layer multicast groups 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 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 RLM - Video Streams One channel per layer Layers are additive Adding more channels gives better quality Adding more channels requires more bandwidth 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 Router Mechanisms Dropping of packets Drop less preferential packets first RLM - Protocol Abstraction on congestion, drop a layer on spare capacity, add a layer 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 RLM – Adding and Dropping layers If join experiment fails If join experiment succeeds Drop layer, since causing congestion One step closer to operating level But join experiments can cause congestion Only want to try when might succeed 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 RLM Join Experiment How to know is join experiment succeeded Detection time 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 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 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 ?? 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) RLM - Evaluation Topologies 1 – explore latency 2 – explore scalability 3 – heterogeneous with two sets 4 – large number of independent sessions RLM – Performance Metrics Worse-case lost rate over varying time intervals Throughput as percent of available But will always be 100% eventually So, look at time to reach optimal Note, neither alone is ok Short-term: how bad transient congestion is Long-term: how often congestion occurs Could have low loss, low throughput High loss, high throughput Need to look at both RLM – Performance Results Latency Results RLM – Performance Results Latency Results RLM – Performance Results Session Size RLM – Performance Results Convergence rate RLM – Performance Results Bandwidth Heterogeneity 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!! Conclusions Overall – a nice architecture and mechanism to regulate traffic and have the best utilization But still needs refinement References S. McCanne, V. Jacobson, and M. Vetterli, "Receiver-driven layered multicast," in Proc. SIGCOMM'96, ACM, Stanford, CA, Aug. 1996, pp. 117--130.