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Multicast Forwarding and
Application State Scalability in
the Internet
Tina Wong
Dissertation Seminar
Computer Science Division
University of California, Berkeley
October 16, 2000
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Challenge
“… in the long run, the biggest issue facing multicast
deployment is likely to be the scalability of
multicast forwarding state as the number of
multicast groups increases.”
--Thaler and Handley 2000
The memory required to store multicast forwarding
entries at a router with 32 interfaces is 1024 TB
for IPv6, assuming 50% address space utilization
--Radoslavov, Govindan and Estrin 1999
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Outline
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Introduction, background, motivation
Multicast state scaling trends in Internet
Preference clustering protocol
Application-driven tunable reliability
Conclusions and future work
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IP Multicast
S
R
R
R
R
R
• Efficient point-tomultipoint delivery
mechanism
• Packets travel on
common parts of the
network only once
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Multicast Routing
S
R
R
Broadcast
DVMRP
R
• Per-source reverse
shortest path tree
• Broadcast-and-prune
• MBone
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Multicast Routing
S
R
R
Prune
DVMRP
R
• Per-source reverse
shortest path tree
• Broadcast-and-prune
• MBone
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Multicast Routing
S
R
R
DVMRP
R
Forward Data
• Per-source reverse
shortest path tree
• Broadcast-and-prune
• MBone
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Multicast Routing
• PIM-Dense Mode / Sparse Mode
– Unidirectional shared tree
– Explicit joins
– Core location a problem
• Core Based Trees (CBT)
– Bi-directional shared tree
– More optimal data paths
– Few routing vendors support
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Multicast Forwarding State
• Router maintains membership
state to achieve forwarding
• State scales linearly with
number of concurrent groups
• No natural aggregation
iif0
s = 0.0.0.0/0
G = 224.0.1.2/32
iif0, oif1, oif2
oif0
oif1 oif2
• Number of concurrent multicast
groups limited by router memory
• Heartbeat messages to maintain
state incur processing costs
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Motivation
Lots of simultaneously active multicast
groups on the Internet?
• Many small, group-based applications
– Few participants form a single multicast group
– E.g. internet video conferencing, games, events
notifications, etc
• Few large-scale applications
– Lots of users form many multicast groups
– E.g. Content delivery, stock quotes, DIS, etc
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Related Work
• Multicast state reduction
– Leaky and non-leaky state aggregation
– Tunneling in backbone (MPLS, DCM)
– Non-branching state elim (DTM, REUNITE)
• Application-level multicast
– End Sytsem Multicast, YOID, Scattercast
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Contributions
• Comprehensive analysis on multicast state
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–
–
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Understand scaling trends in the Internet
Predict future growth
Estimate potentials for reduction
Apply to network provisioning, protocol and
application design
• Mechanisms for network and end-host
state scalability in large-scale applications
– Interest-based content delivery
– Application-driven loss recovery
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Outline
•
•
•
•
•
Introduction, background, motivation
Multicast state scaling trends in Internet
Preference clustering protocol
Application-driven tunable reliability
Conclusions and future work
13
Questions: Scaling Trends
Much research and engineering effort into
making IP multicast widely deployed...
• How do multiplying peering agreements among
parallel backbone networks affect multicast state
scalability?
• How do rising subscriptions to individual
applications increase multicast state?
• What are the state scaling properties when more
and more applications use multicast?
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Questions: State Concentration
An intuition: multicast state scalability is
most critical at “core” routers…
• How concentrated is
multicast state at
“core” routers?
“Core”
• How much benefit from
tunneling?
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Questions: State Reduction
An intuition: delivery trees of sparse
multicast groups tend to have large number
of non-branching routers...
S
• How prominent are
non-branching routers?
• Are these routers
stateful?
R
R
R
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R
Basic Model
Local state
• Fraction of concurrent
multicast groups
True local state
• Local state with only
multicast forwarding
Independent of address
space size and number
of concurrent groups
iif0
oif0
oif1 oif2
5 concurrent groups
Local state = 2/5
True local state = 1/5
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Methodology
• Simulations
– Extends upon SGB package
• Parameters
– Topology
– Session density
– Membership model
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Topology
• 4 AS graphs from Nov97 to Jan00
– Connectivity among Internet autonomous systems
– Study multicast state at inter-domain level
– Over 3 year timespan
• Mbone graph from Feb99
– Study multicast state at intra-domain level
• Generated graphs
– TIERS
– Transit-stub
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Session Density
• Graphs have different number of nodes,
from 1000 to 6474
– Session density instead of absolute size
– 0.1% to 0.9%, 1% to 9%, 10% to 90%
– E.g., session with 0.1% density in AS-Jan00
with 6500 nodes involves 7 domains
– E.g., session with 10% density in Mbone with
4200 nodes involves 420 routers
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Membership Taxonomy
Topological Correlation
within one group
NO
NO
1
random
Subscription correlation
across multiple groups
YES
4
interest
YES
2
3
distr
affinity/
clusters disaffinity
5
6
layered
interest
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Experiments
• For each experiment, fix topology, session
density and membership model
– (1) Pick a set of nodes with these parameters
– (2) Build shortest path tree rooted at a random
node from this set
– Repeat (1) & (2) 1000 times
– Calculate local state and true local state on
each node in topology
• All combinations of parameters used,
yielding 945 experiments and results!
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Answers: Scaling Trends 1
• How do multiplying peering agreements
among parallel backbone networks affect
multicast state scalability?
– More state at a handful of core routers
– Offset by reduced state in majority of routers
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Topological Properties
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Hypothesis
• In a more connected network
– Trees have larger fanouts and shorter heights
– Only a few highly peered routers involved in
most concurrent multicast trees
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Hypothesis
• In a less connected network
– Trees have smaller fanouts and taller heights
– Backbone routers share responsibility of
multicast forwarding -- “load balancing”?
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Path Lengths
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Node Degrees
AS-Nov97
MBone
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Past and Future ScalingTrends
• Implication
– If Internet continues to evolve as it has been,
multicast memory requirements at most of
border routers actually decline, all things
remain equal
• Evidence
– Peering increases for past 3 years
– Maximum domain degree from 605 to 1459,
roughly 50% expansion each year
– Slight decrease in state for majority of nodes
– Slight increase for rest of nodes
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Answers: Scaling Trends 2
• How do rising subscriptions to individual
applications affect multicast state?
– Follows power law
• fraction of stateful routers grows proportional to
some constant power of multicast group size
– Exponents within each membership for the
Internet similar over past 3 years
– Predictive of future state growth
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Answers: State Concentration
• How concentrated is multicast state at the
“core” routers?
– State concentration does not follow “10/90”
rule even when session density is 0.1%
– Application-driven membership significantly
impact state distribution and concentration
– Tunneling useful for multicast applications with
very sparse and spread-out membership
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Answers: State Reduction
• How prominent are non-branching routers?
Are these routers stateful?
– Very prominent
– Up to 2 orders of magnitude reduction is
possible even at top 10% most stateful nodes
– Substantial even at 90% session density
– Promising approach
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Outline
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•
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Introduction, background, motivation
Multicast state scaling trends in Internet
Preference clustering protocol
Application-driven tunable reliability
Conclusions and future work
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Large-scale Applications
• Large-scale applications: many receivers,
many sources, rich data types, UI
• Multicast uses one data stream to satisfy
potentially heterogeneous receivers
• Lead to Preference Heterogeneity
– Users differ in interest on application data
– E.g. Content delivery, news dissemination, stock
quotes, network games, DIS, etc
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Example: Stock Quotes Service
...
AABC
BACU
CABL
DAGR
EACO
FACO
www.StockCentral.com
INTC
DELL
CSCO
MSFT
Amy
AAPL
AMZN
EWEB
MSFT
GABC
QCOM
Bob
PWBC
SISI
YHOO
...
Cathy
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Example: Network Games
A player's position in virtual environment
drives its preferences on entity updates
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Preference Heterogeneity
• Assign each logical data stream a unique
multicast address ?
+ No superfluous data
– Multicast routing state scalability
– Multicast address allocation and scarcity
– End-host connection maintenance
• 100% reliability not necessary
– Different levels of reliability desired
– Help to reduce NACK implosion
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The Clustering Concept
complete
heterogeneity
many small
groups
UNICAST
complete
similarity
MULTICAST
CLUSTER
approximately similar sources and receivers
into like groups
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Preference Clustering Protocol
• Clustering algorithm
– On-line and adaptive to changes in preferences
– Customizable to different application and data
types
• Signaling protocol
– Coordinate clustering within an application
– Scalable, fault tolerant and reliable through
decentralization, soft state and sampling
• API
• Detailed evaluation
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App-Level Tunable Reliability
• Consider application semantics in loss
recovery decisions
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Meta-data to describe data content
Temporal: statistics on update frequency
Semantic: magnitude or importance of change
Policy-driven by individual receivers
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Outline
•
•
•
•
•
Introduction, background, motivation
Multicast state scaling trends in Internet
Preference clustering protocol
Application-driven tunable reliability
Conclusions and future work
45
Conclusions
• Comprehensive study on multicast state
scalability
– Scaling trends confirmed with past 3 years
– State distribution and concentration
– Potentials for reduction
• Mechanisms to accommodate problem for
large-scale applications
– Customizable and adaptive preference
clustering protocol
– Tunable reliable multicast protocol
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Future Directions
• Compare and contrast methodologically IP
multicast and application-level multicast
– Params: Topology, session density, membership
– Apps: Few-to-few, one-to-many
– Metric: Bandwidth, latency, complexity, etc
• Placement of service agents in Internet
– Spawning of new agents
– Coalescing based on topology, user population,
network measurements
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