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Receiver-Driven Layered Multicast
Steve McCanne and Van Jacobson, LBL
One-line summary: Each IP mcast group adds an incremental
"layer" of a multimedia session transmission. Receiver's decision to
subscribe/unsubscribe groups is based on periodically running
adaptive-timer-controlled "experiments" to see if adding a layer will
cause congestion. Other receivers near you observe your experiments,
learn from them, and conditionally suppress their own experiments when
others in progress, so number of experiments doesn't grow linearly with
number of participants.
- Idea: deliver multiple layers of multimedia/realtime signals on
different "channels" (IP mcast grps).
- RLM focuses on
"cumulative" (adding layers increases quality incrementally) but
can also work with "simulcast" (each channel carries a complete
copy of the signal, but at different qualities/rates).
- Why not use priority-dropping of packets to achieve?
Priority-drop curve has no unique maximum (see fig 2 in paper),
therefore no convergence to single stable operating point, and a
misbehaving user has no incentive to reduce the requested rate so
would drive network into constant congestion.
- RLM doesn't ensure fairness itself, but should coexist well with
router-based schemes that do.
- Join experiment: test to see if adding another layer will improve
your quality or cause congestion (lower your quality).
- Separate join-timer w/exponential backoff used for each
layer of subscription.
- If join-experiment outlasts detection-time without
congestion, experiment is successful and the layer is
added. If expt. fails, the layer is quickly removed.
Detection time and its variance are tracked and adapted
using failed join-expts.
- Scaling: "shared learning". Local receivers listen and
learn from (adjust their join-timers from) local peers'
- Local receivers suppress starting an expt if they detect
one in progress (reduce probability of expts interfering
- Exception: allow overlap of an expt. at same or lower
level with one already in progress, to allow newer
receivers with lower subscription levels to converge to
optimum point more quickly.
- Protocol operation:
- Formally described by a 4-state machine: steady state,
measurement, drop-layer. See state diag. in fig 6.
- Join-timer backoff: multiplicative increase of join
timer when an expt fails, clamped to a maximum which is
based on current number of receivers. This qty is
estimated from RTCP control messages, so that
aggregate join-expt rate is fixed indept of
- Join-timer relaxation: (slower) geometric decrease by Beta
at every detection-timer interval, clamped to a minimum.
- Detection-time estimate: since detection is the time
between initiation of an experiment and feedback as to
what it caused, detection time is computed by correlating
failed-experiment start times with onset of congestion.
Congestion is detected by latency measurements from failed
experiments, passed through a low-pass filter.
- Claim: don't necessarily show that RLM is absolutely
scalable, but do show that scaling behavior matches
authors' intuitions in coming up with the mechanisms.
- CBR streams have interarrival times that are constant
perturbed by zero-mean noise process. Fails to capture
burstiness in video streams, but bursty
sources can be smoothed by applying rate control.
(Therefore not clear how applicable RLM is to bursty
- Simulated 4 different topologies (fig 7):
- point to point
w/bottleneck link (PP), one source, one receiver
- PP with one "endpoint" being a
subtree (LAN), (one source, many receivers)
- PP with subtrees hanging off a couple of
intermediate links (one source, many receivers)
- PP with both endpoints being subtrees: many
sources, many receivers
- Latency scalability: as expected, packet loss rate
increases rapidly (exponentially!) with join/leave
latency, since it takes longer for failed experiments to
be stopped and timers to be backed off, prolonging
- Session scalability (topology 2):
packet loss rate is about constant independent of number
of receivers. Therefore shared-learning mechanisms seem
to be aiding scalability.
- Bandiwdth heterogeneity:
packet loss rate is about constant across receivers with
many different bandwidth constraints, though short-term
congestion periods slightly larger at larger session
- Superposition of sessions (topology 4): when independent
single-source/single-receiver sessions share a common
link, the bottleneck link utilization steadied out at
close to 1, but often was unfair (though there wasn't
- Network implications/assumptions:
- All users assumed to cooperate
- Performance depends critically on mcast join/leave latency
- When multiple sources, "good" performance not well defined
(partitioning across sessions or users?). Similarly: what
is "good" interaction with TCP?
- Interesting future work: since video compression used produces a
prefix code, can partition bit rate arbitrarily among layers, and
vary allocation dynamically.
Elegant leveraging of mcast routing work (can assume traffic only
flowing on a branch if there's an interested receiver downstream) and
application of SRM-like ideas (receiver-based "learn from your peers"
mechanisms) to achieve a scalable and versatile heterogeneous-network
multimedia transmission scheme.
- Bursty sources - ugh
- Interaction of multiple RLM sessions that share some but not all
of the multicast tree? What effects on fairness?
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