1 Internet Multicasting Copyright M. Faloutsos Copyright M. Faloutsos.

1

Internet MulticastingInternet Multicasting

Copyright M. FaloutsosCopyright M. Faloutsos

2U. of Toronto Michalis Faloutsos

The Multicast ProblemThe Multicast Problem

GIVEN:GIVEN: NetworkNetwork

GIVEN: User Requests (and quality requirements)GIVEN: User Requests (and quality requirements)

ESTABLISH: ESTABLISH: EfficientEfficient distribution trees distribution trees

?

3

What is The Need for Multicasting?What is The Need for Multicasting?

Use bandwidth efficientlyUse bandwidth efficiently• Replace many unicast connections Replace many unicast connections

with one multicast connectionwith one multicast connection• Duplicate packets only when Duplicate packets only when

necessarynecessary

Simply put:Simply put:• Send one packet per link instead of Send one packet per link instead of

multiplemultiple• Some links can not support multiple Some links can not support multiple

flows of the same dataflows of the same data

3 times thetraffic

4

What Is Efficient?What Is Efficient?

ScalableScalable• Multicast stateMulticast state• Number of control messagesNumber of control messages

Use network resources efficientlyUse network resources efficiently

Meet end-to-end performance requirementsMeet end-to-end performance requirements• E2e Delay: from source to destinationsE2e Delay: from source to destinations

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Some Theory on Multicast RoutingSome Theory on Multicast Routing

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Multicast and the Steiner TreeMulticast and the Steiner Tree

Steiner tree problem: Steiner tree problem: • Given an undirected weighted graph G(V,E), and a Given an undirected weighted graph G(V,E), and a

set of nodes S, find the minimum cost tree that spans set of nodes S, find the minimum cost tree that spans the nodes in Sthe nodes in S

It is similar to a multicast problem which isIt is similar to a multicast problem which is• Centralized: (link state)Centralized: (link state)• Static: we know all group members ahead of timeStatic: we know all group members ahead of time• Undirected graph: same edge weight either directionUndirected graph: same edge weight either direction

7

A Steiner Tree ExampleA Steiner Tree Example

Which is the Steiner tree for green and red Which is the Steiner tree for green and red nodes?nodes?

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10

2

11

4

2

53 3

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A Steiner Tree Example: SolutionA Steiner Tree Example: Solution

Shortest Path treeShortest Path tree =/= =/= Steiner TreeSteiner Tree

14 + 13 =/= 16 14 + 13 =/= 16

1010

10

2

11

4

2

53 3


Multicast Routing AlgorithmsMulticast Routing Algorithms

NP-complete problem NP-complete problem [Dreyfus72][Dreyfus72]

Approximation algorithms have practical interestApproximation algorithms have practical interest

Shortest Paths Greedy Optimal

source

destinations

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The Greedy AlgorithmThe Greedy Algorithm

Start from the sourceStart from the source(or pick one group member)(or pick one group member)

While not all connectedWhile not all connected• Find destination closest to Find destination closest to

partial treepartial tree• Add destination and relate Add destination and relate

pathpath

Note: tree-node distanceNote: tree-node distancemin dist(Any-tree-node, node)min dist(Any-tree-node, node)

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The KMB AlgorithmThe KMB Algorithm

A Greedy Variation A Greedy Variation [Kou Markowski Berman 81][Kou Markowski Berman 81]

Greedy: connect to nearest point on the treeGreedy: connect to nearest point on the tree

KMB: connect to nearest group memberKMB: connect to nearest group member

Seemingly: KMB is less efficient that GreedySeemingly: KMB is less efficient that Greedy

Surprisingly: KMB is just as efficient!Surprisingly: KMB is just as efficient!

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How KMB WorksHow KMB Works

Create complete distance graph of group members G’Create complete distance graph of group members G’Calculate Minimum Spanning Tree, T’, in G’Calculate Minimum Spanning Tree, T’, in G’Translate T’ to a tree T in GTranslate T’ to a tree T in G

1110

10

2

11

1

1

32 3

10 1210

3

2

5

1310

11

1

2

G G’ and T’ T

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The Performance of AlgorithmsThe Performance of Algorithms

Competitive Ratio of an Algorithm: Competitive Ratio of an Algorithm: • r = max( T_algorithm / OPT )r = max( T_algorithm / OPT )• Over all possible problem instances (worst case)Over all possible problem instances (worst case)

Inuitively,Inuitively,• Given any problem instance, the T_algorithm will Given any problem instance, the T_algorithm will

never be more expensive than r x optimal OPT.never be more expensive than r x optimal OPT.

Express ratio as a function of problem sizeExpress ratio as a function of problem size• N number of nodes in graphN number of nodes in graph• M number of group members (not counting source)M number of group members (not counting source)

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Competitive Ratio of Shortest PathsCompetitive Ratio of Shortest Paths

Never worse than M:Never worse than M:• T <= M OPTT <= M OPT

Assume Dmax the Assume Dmax the maximum distance maximum distance between source and between source and destinationsdestinations

OPT => DmaxOPT => Dmax

T <= M DmaxT <= M Dmax

Dmax

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KMB Has Good PerformanceKMB Has Good Performance

We can prove that the KMB tree is less than 2 We can prove that the KMB tree is less than 2 the cost of the optimal Steiner treethe cost of the optimal Steiner tree

KMB < 2 OPTKMB < 2 OPT

Where does this 2 come from?Where does this 2 come from?

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Competitive Ratio of KMBCompetitive Ratio of KMB

Take Steiner, cost OPTTake Steiner, cost OPT

There is a There is a Walk Walk of OPT tree:of OPT tree:• Walk = 2 OPTWalk = 2 OPT• Walk defines sequence of v_iWalk defines sequence of v_i

Split the walk in W(v_i, v_i+1)Split the walk in W(v_i, v_i+1)

Intuitively, KMB will create a Intuitively, KMB will create a tree of cost less than the walktree of cost less than the walk

1

2

34

D(1,2)

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Competitive Ratio of KMB (cont.)Competitive Ratio of KMB (cont.)

Intuitively, T_KMB <= Walk Intuitively, T_KMB <= Walk

Split Split walkwalk in W(v_i, v_i+1) in W(v_i, v_i+1)

W() form a tree W() form a tree T_w T_w < 2 OPT< 2 OPT

T_w corresponds to T_w corresponds to T_w’T_w’ in in the complete distance graphthe complete distance graph• T_w’T_w’ <= <= T_wT_w

T_KMB is MST in that graph:T_KMB is MST in that graph:• T_KMB <= T_KMB <= T_w’T_w’

T_KMB < Walk = 2 OPTT_KMB < Walk = 2 OPT

1

23

4W(2,3) W(2,3)

D(2,3) < W(2,3), since D() is distance

D(2,3)

Complete distance Graph of KMB


Multicasting on Directed Graphs Multicasting on Directed Graphs

Real Networks: DirectedReal Networks: Directed

Maximum AsymmetryMaximum Asymmetry: : • Ratio of weights of two opposite edgesRatio of weights of two opposite edges• AA = max( w1 / w2 ) = max( w1 / w2 ) • Over all pairs of nodesOver all pairs of nodes

Undirected graphs: A = 1Undirected graphs: A = 1

Goal: Min. Cost Directed TreeGoal: Min. Cost Directed Tree

Joint work: R. Pankaj, K.C. Sevcik

c

Ac

w2

w1

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Multicast BoundsMulticast Bounds

Bounds for Greedy and Sh. Paths w.r.t.:Bounds for Greedy and Sh. Paths w.r.t.:• Maximum asymmetry: Maximum asymmetry: AA• Number of join requests: Number of join requests: MM• Competitive Ratio of tree cost: Algorithm/OptimalCompetitive Ratio of tree cost: Algorithm/Optimal

Problem Variations:Problem Variations:• Static: members know ahead of timeStatic: members know ahead of time• Join-Only: members join randomlyJoin-Only: members join randomly• Join-Leave: members join and leave randomlyJoin-Leave: members join and leave randomly

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Performance On Undirected GraphsPerformance On Undirected Graphs

Shortest Path: tighlty bounded by MShortest Path: tighlty bounded by M• Where M the number of destinationsWhere M the number of destinations

Greedy and KMB: Greedy and KMB: • Static: bounded by 2Static: bounded by 2• Join-only: log MJoin-only: log M• Join-leave: 2^M (or N)Join-leave: 2^M (or N)

21Michalis Faloutsos

Overview of Theoretical ResultsOverview of Theoretical Results

Short. Paths:Short. Paths:• tight bound in all variationstight bound in all variations

Greedy:Greedy:• Static: Tight bound (2 A) Static: Tight bound (2 A) • Join-Only: Near-optimal online bound (A log M)Join-Only: Near-optimal online bound (A log M)

Greedy is no more than log M from Greedy is no more than log M from any onlineany online algorithm algorithm• Join-Leave: Large lower bound (A or 2^M)Join-Leave: Large lower bound (A or 2^M)

[Faloutsos et al SIROCCO’97][Faloutsos et al SIROCCO’97]


Our Results in DetailOur Results in Detail

Static Join-Only Join-Leave

Sh. PathsTight

M M M

GreedyUpper Bound

min (A, M) min (M, A log(M)) *

GreedyLower Bound

min (A, M) min (M, A log(M)/log(A+1))max (A, 2^M)

• Lower bound for any online algorithm • Greedy at most log(M) to any online algorithm

Join-Only:


A Graphical OverviewA Graphical Overview

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Static Join-Only

Join-Leave

Greedy SP Static and Join-only:Static and Join-only:• Greedy is good (Greedy is good (near-optimalnear-optimal))

Join-leave:Join-leave:• Greedy bad in worst caseGreedy bad in worst case

Why not combine into an Why not combine into an adaptiveadaptive algorithm? algorithm?

2A

M

A,2^M


Desirable Protocol PropertiesDesirable Protocol Properties

ScalableScalable

Easy to implement and deployEasy to implement and deploy

Provide high QoSProvide high QoS

Use network resources efficientlyUse network resources efficiently• Create efficient treesCreate efficient trees• Use little bandwidthUse little bandwidth• Minimize required stateMinimize required state

Adaptive - FlexibleAdaptive - Flexible

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Internet Multicasting: ProtocolsInternet Multicasting: Protocols

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Breaking the RulesBreaking the Rules

Internet: no state Internet: no state per flowper flow

Multicasting has Multicasting has state per each state per each multicast group!multicast group!

G1, G2

G1, G2

G1, G2

G1

G2

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What Are The Issues?What Are The Issues?

RoutingRouting• Intra-domainIntra-domain• Inter-domainInter-domain

Reliable multicastingReliable multicasting

Secure multicastingSecure multicasting

Address space allocationAddress space allocation

Multicast state aggregationMulticast state aggregation

Modeling and Simulating: realistic modelsModeling and Simulating: realistic models

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What Is The Real IssueWhat Is The Real Issue

ISPs do not want/care to deploy multicastISPs do not want/care to deploy multicast

Chicken and egg problemChicken and egg problem• Customers do not ask for multicasting,Customers do not ask for multicasting,

because there are no multicast applications,because there are no multicast applications,

because none builds multicast applications,because none builds multicast applications,

because there is no support for multicastingbecause there is no support for multicasting

Multicast researchers:Multicast researchers:• Be careful in what topic you chooseBe careful in what topic you choose

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MBone: The multicast backboneMBone: The multicast backbone

Some routers are multicast enabledSome routers are multicast enabled

These routers connect to each other with These routers connect to each other with virtual edges (encapsulation)virtual edges (encapsulation)

Multicast protocols and applications can be Multicast protocols and applications can be deployed on the MBonedeployed on the MBone

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Multicast Routing Multicast Routing

Router Assisted: routers duplicate packetsRouter Assisted: routers duplicate packets• MOSPF: Link-state multicast routingMOSPF: Link-state multicast routing• DVMRP: flood and pruneDVMRP: flood and prune• PIM/CBT: core based, shared treePIM/CBT: core based, shared tree• Source Specific Multicast: each source creates a treeSource Specific Multicast: each source creates a tree• QoS aware protocols: QoSMIC, QMRPQoS aware protocols: QoSMIC, QMRP

Application-levelApplication-level• Only end-points (members) are aware of the treeOnly end-points (members) are aware of the tree• Packets are duplicated at the members only!Packets are duplicated at the members only!

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MOSPF: Link State multicastingMOSPF: Link State multicasting

Link-state: advertise all your linksLink-state: advertise all your linkseach router advertises groups for which it each router advertises groups for which it has local receivershas local receiversEach router executs the same algorithm:Each router executs the same algorithm:• They establish a tree accordinglyThey establish a tree accordingly

Scalability issues: Scalability issues: • Membership change: new advertisements are Membership change: new advertisements are

sentsentIETF RFC 1548 StandardIETF RFC 1548 Standard

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The PIM ProtocolThe PIM Protocol

Most deployed protocolMost deployed protocol

Based on a Core – Based on a Core – Rendez-vous PointRendez-vous Point

Based on Shortest Path Based on Shortest Path RoutingRouting

Issue: selecting the coreIssue: selecting the core

Not adaptiveNot adaptiveNew

source

core


Border Gateway Multicast ProtocolBorder Gateway Multicast Protocol

Designed to be interdomainDesigned to be interdomain

Border Gateway Multicast ProtocolBorder Gateway Multicast Protocol [Thaler 97][Thaler 97]

• Reverse Reverse Shortest Paths Routing Shortest Paths Routing • Domains “own” multicast addressesDomains “own” multicast addresses

Allocating addresses needs care: MASCAllocating addresses needs care: MASC

New

Shortest Path

source


Yet Another Multicast Protocol: YAMYet Another Multicast Protocol: YAM

Yet Another Multicast protocolYet Another Multicast protocol [Carlberg97][Carlberg97]

Multiple pathsMultiple paths

Use of Use of static informationstatic information

Non scalableNon scalable::• ““flood” of control messagesflood” of control messagesNew


Our Protocol: QoSMICOur Protocol: QoSMIC

QoSQoS MMulticast ulticast IInternet protonternet protoCCol ol [SIGCOMM’98][SIGCOMM’98]

Supports QoS Supports QoS

Uses dynamic informationUses dynamic information

ScalableScalable


QoSMIC: The OverviewQoSMIC: The Overview

The protocol has three phases:The protocol has three phases:

1. Search: 1. Search: for Candidatesfor Candidates

2. Bid: 2. Bid: Candidates “Bid”Candidates “Bid”

3. Select: 3. Select: New chooses pathNew chooses path• using dynamic QoS info.using dynamic QoS info.

Joint work with A. Banerjea, R. Pankaj

New


I. Local Search (costly) II. Multicast Tree Search Bid messages collect

dynamic information

QoSMIC: Search and BidQoSMIC: Search and Bid

Manager


In Searching: - Local and/or M. Tree Search

In Routing:- Greedy and/or Short. Paths

In run time according to application needs

QoSMIC: Flexible-AdaptableQoSMIC: Flexible-Adaptable

Manager


Some SimulationsSome Simulations

Compare routing efficiency with BGMPCompare routing efficiency with BGMP• Tree cost = Sum of link-weights Tree cost = Sum of link-weights

Compare message complexity with YAMCompare message complexity with YAM• Number of control messages of search phase Number of control messages of search phase


The Simulation Set-upThe Simulation Set-up

Build a simulatorBuild a simulator

Real Graph: Major Internet routers Real Graph: Major Internet routers [[Casner93Casner93]]

Weighted asymmetric graphWeighted asymmetric graph

Simulation Precision:Simulation Precision:• 95%-confidence interval <10% of shown values95%-confidence interval <10% of shown values


The Simulated AlgorithmsThe Simulated Algorithms

Offline: greedy for referenceOffline: greedy for reference• At any snapshot, run greedy from scratchAt any snapshot, run greedy from scratch

SP: Shortest PathsSP: Shortest Paths

BGMP: Reverse Shortest PathsBGMP: Reverse Shortest Paths

QoSMICQoSMIC


Tree Cost Vs Group SizeTree Cost Vs Group Size

0.8

1

1.2

1.4

1.6

1.8

0.1 0.2 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.7 0.7 0.8 0.8 0.9 1.0

Group Size %

Cost

SPOfflineQoSMICBGMP

QoSMIC up to 40% better than BGMP QoSMIC up to 40% better than BGMP

Asym = 10


Tree Cost Vs AsymmetryTree Cost Vs Asymmetry

QoSMIC up to 60% better than BGMPQoSMIC up to 60% better than BGMP

0.8

0.9

1

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1.2

1.3

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1 6 11 16 21 26 31 36 41 46

Asymmetry

Cost

SPOfflineQoSMICBGMP


Joinning Distance DistributionJoinning Distance Distribution

Even Even smallsmall Local Search is good (2 h - 55% joins) Local Search is good (2 h - 55% joins)

Large Large Local Search (8 h) to cover for ALL casesLocal Search (8 h) to cover for ALL cases

Group Density:1-15 %


Search Message ComplexitySearch Message Complexity

-2000

200400600800

100012001400160018002000

0 2.5 3 3.5 4 4.5 5Average Degree

Messages per Join

YAM

QOSMIC

YAM has to have large Local SearchYAM has to have large Local Search

QoSMIC keeps Local Search small QoSMIC keeps Local Search small scalablescalable

Over-estimate of both protocols


QoSMIC: The ClaimQoSMIC: The Claim

<< QoSMIC routes in a QoS-sensitive way, and is more efficient even than the non QoS-sensitive protocols >>

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Why Isn’t QoSMIC the Standard?Why Isn’t QoSMIC the Standard?

Implementation complexityImplementation complexity

Scalability and message complexityScalability and message complexity

Needs industry and IETF supportNeeds industry and IETF support

Timeliness:Timeliness:• Now people look at other issuesNow people look at other issues

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Generalizing QoSMICGeneralizing QoSMIC

QoSMIC as a set of searching proceduresQoSMIC as a set of searching procedures• Local flooding: receiver drivenLocal flooding: receiver driven• Indirect search: through designated routerIndirect search: through designated router

A mechanism to search for points of interestA mechanism to search for points of interest• Servers, caches etcServers, caches etc

Possible applicationsPossible applications• Web server locationWeb server location• Ad-hoc networks: searching for a destinationAd-hoc networks: searching for a destination

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Multicasting: The ResurrectionMulticasting: The Resurrection

Can we do multicasting without the need of Can we do multicasting without the need of router support?router support?

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Application Level MulticastingApplication Level Multicasting

Push the “complication” to Push the “complication” to the end-usersthe end-usersSeveral schemes (NARADA, Several schemes (NARADA, YOID)YOID)Group members keep track of Group members keep track of each othereach other• A new node joins at other A new node joins at other

membersmembers• Network is unaware of Network is unaware of

multicastmulticast

Trade-off:Trade-off:• We need to keep track of We need to keep track of

membersmembers• We may waste bandwidth We may waste bandwidth

(early duplication)(early duplication)

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Application Layer Multicast:Application Layer Multicast:PerformancePerformance

A-L multicast implements the KMB version of A-L multicast implements the KMB version of the greedy algorithm!the greedy algorithm!

Thus routing performance is not that bad!Thus routing performance is not that bad!• In theory: cost of an edge is fixedIn theory: cost of an edge is fixed

The real concern:The real concern:• We overload the last-mile of the networkWe overload the last-mile of the network

I.e. imagine 3 copies of video uploadedI.e. imagine 3 copies of video uploaded

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Application Layer Multicasting:Application Layer Multicasting:Pros and ConsPros and Cons

Pros:Pros:

Easy to deployEasy to deploy

Adaptive to changeAdaptive to change

Good routing Good routing

Cons:Cons:

Need to track group membersNeed to track group members

Member-changes alter the treeMember-changes alter the tree

Bad bandwidth utilizationBad bandwidth utilization

More message overheadMore message overhead

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Application Layer Multicasting:Application Layer Multicasting:ApplicationsApplications

Ad hoc networks:Ad hoc networks:• Paths are changing rapidly, faster than membersPaths are changing rapidly, faster than members

Nodes need to keep dynamic routing state Nodes need to keep dynamic routing state anywayanyway

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Aggregating Multicast StateAggregating Multicast State

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Large Routing State is a ProblemLarge Routing State is a Problem

Internet scalability: based on stateless Internet scalability: based on stateless routingrouting

IP multicast: state on each tree router per IP multicast: state on each tree router per group (group = connection)group (group = connection)

Routing State: proportional to # groupsRouting State: proportional to # groups

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Previous Attempts to Reduce StatePrevious Attempts to Reduce State

General idea:General idea:• First, allocate resources: addresses and treesFirst, allocate resources: addresses and trees• Then, try to aggregate state on routersThen, try to aggregate state on routers

IF addresses consecutive AND topology matchesIF addresses consecutive AND topology matchesMASC/BGMP: Thaler, Estrin, Handley et al sigcommMASC/BGMP: Thaler, Estrin, Handley et al sigcomm

INFOCOM’00: Thaler, HandleyINFOCOM’00: Thaler, Handley

USC-TR 99-697: Radoslavov, Estrin, GovindanUSC-TR 99-697: Radoslavov, Estrin, Govindan

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Example of Traditional AggregationExample of Traditional Aggregation

Router finds that Router finds that groups G1, G2 have:groups G1, G2 have:• Consecutive addressesConsecutive addresses• Same outgoing linksSame outgoing links

Aggregates groupsAggregates groups

Leaky aggregation: not Leaky aggregation: not all links match exactlyall links match exactly

G1, G2

G1, G2

G1, G2

Keep one entry for G1,G2: 1,3,5Keep one entry for G1,G2: 1,3,5

G1

Leaky: Send G2 data to link 4!Leaky: Send G2 data to link 4!

1122

33 44

55

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Our Idea: Forced AggregationOur Idea: Forced Aggregation

Force groups to share a distribution treeForce groups to share a distribution tree• First aggregateFirst aggregate• Then routeThen route

Trade-off bandwidth for routing stateTrade-off bandwidth for routing state

Limitation: know the group membersLimitation: know the group members

Target: intra-domain useTarget: intra-domain use

•Joint work: A. Fei, J. Cui, and M. Gerla, UCLAJoint work: A. Fei, J. Cui, and M. Gerla, UCLA

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Benefits of aggregationBenefits of aggregation

State reductionState reduction• Less memoryLess memory• Faster look-up timeFaster look-up time

Traffic engineering: (not discussed here)Traffic engineering: (not discussed here)• Manage and Control trafficManage and Control traffic• Support Quality of ServiceSupport Quality of Service

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RoadmapRoadmap

IntroductionIntroduction

Our approach in more detailOur approach in more detail

Implementation issuesImplementation issues

Analytical results Analytical results

Simulations resultsSimulations results

ConclusionsConclusions

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A Simple Version of the ProblemA Simple Version of the Problem

GivenGiven• a network a network • a set of trees (a set of trees (trunkstrunks))• a new multicast groupa new multicast group

Find Find • the trunk that covers the the trunk that covers the

group with minimum group with minimum bandwidth overheadbandwidth overhead

• or create new trunk if or create new trunk if Bandwidth OverheadBandwidth Overhead is is above a above a ThresholdThreshold

White node receives packets!= bandwidth overhead

Group-specificblock!

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Subtle Points and VariationsSubtle Points and Variations

We need to know the group members at least We need to know the group members at least approximatelyapproximatelyOff-line versus on-line version of the problemOff-line versus on-line version of the problem• Know all groups ahead of timeKnow all groups ahead of time

Perfect versus leaky match:Perfect versus leaky match:• Trunk and group have the same leavesTrunk and group have the same leaves

Group specific blocks in the trunkGroup specific blocks in the trunk• Block unwanted traffic on a per Block unwanted traffic on a per sourcesource/group basis/group basis

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Quick Reconfiguration - Fault Quick Reconfiguration - Fault ToleranceTolerance

Quick error recoveryQuick error recovery

Imagine having multiple Imagine having multiple trunks that cover the trunks that cover the same nodessame nodes

Initially: red group is Initially: red group is using using yellowyellow tree tree

If If yellowyellow tree fails tree fails

I switch to I switch to greengreen tree tree• Change “labels” at end Change “labels” at end

pointspointsMap group packet to trunkMap group packet to trunk

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Implementation IssuesImplementation Issues

We need controlled and centralised We need controlled and centralised operationoperation• Some nodes should keep track of trunks and groupsSome nodes should keep track of trunks and groups

How can we force a group on a tree?How can we force a group on a tree?• IP EncapsulationIP Encapsulation• Multi Protocol Label Switching (MPLS)Multi Protocol Label Switching (MPLS)• Home-made solutions are being consideredHome-made solutions are being considered

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Aggregation Performance MetricsAggregation Performance Metrics

Average aggregation degree:Average aggregation degree:• #groups / #trunks#groups / #trunks

Routing state reduction ratio, r_as: Routing state reduction ratio, r_as: • 1 – N_agg /N_non-agg1 – N_agg /N_non-agg

Non-reducible state: Non-reducible state: • the state that leaf nodes maintain the state that leaf nodes maintain

Reducible state reduction ratio, r_rs:Reducible state reduction ratio, r_rs:• As r_as without considering the non-reducible stateAs r_as without considering the non-reducible state

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Preliminary Simulation ResultsPreliminary Simulation Results

Goal: examine trade-offGoal: examine trade-off• aggregation vs bandwidth overheadaggregation vs bandwidth overhead

Used Abilene network topology Used Abilene network topology

Random selection of group membersRandom selection of group members• No assumption of localityNo assumption of locality

We did not consider group-specific blockingWe did not consider group-specific blocking

Group size: 2-10 nodes uniformlyGroup size: 2-10 nodes uniformly

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How Many Groups per Tree?How Many Groups per Tree?

Average aggregation degree vs. number of groupsAverage aggregation degree vs. number of groups

13 groups per trunk for 10% bandwidth overhead13 groups per trunk for 10% bandwidth overhead

5

20

45bo =0.3

bo =0.2

bo =0.1

bandwidthoverhead

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How Much State Aggregation?How Much State Aggregation?

ReducibleReducible state reduction ratio vs. #groups state reduction ratio vs. #groupsUp to 70% for 10% bandwidth overheadUp to 70% for 10% bandwidth overhead

bo =0.3

bo =0.2

bo =0.1

bandwidthoverhead

0.1

1

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SummarySummary

A novel idea, “forced” aggregate multicast:A novel idea, “forced” aggregate multicast:• First aggregate, then routeFirst aggregate, then route

We examined the trade-off:We examined the trade-off:• Aggregation versus bandwidth overheadAggregation versus bandwidth overhead

Initial simulations are promising Initial simulations are promising

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Future WorkFuture Work

Analytical boundsAnalytical bounds

Simulation with large ISP topologiesSimulation with large ISP topologies

Consider user localityConsider user locality• Find a realistic model for membershipFind a realistic model for membership• Examine its effect on aggregation (positive)Examine its effect on aggregation (positive)

Consider group-specific blocksConsider group-specific blocks

Evaluate different implementationsEvaluate different implementations

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End of MulticastEnd of Multicast

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Total State ReductionTotal State Reduction

State reduction vs. number of groupsState reduction vs. number of groups14-19% for many groups14-19% for many groups

0.2

0

bo =0.3

bo =0.2

bo =0.1

bandwidthoverhead

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Aggregate Multicast for Traffic Aggregate Multicast for Traffic EngineeringEngineering

Customer wants QoS Customer wants QoS guarantees through a guarantees through a networknetwork

Allocate a tree + Allocate a tree + resourcesresources

Customer can utilize Customer can utilize the resource as she the resource as she wantswants

ISP network


QoSMIC: More QoS supportQoSMIC: More QoS support

Multiple sources per group Connect directly to the source to improve

end-to-end QoS

source-1source-2

s-1 only

stop s-1

source-1source-2

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Then, why is not QoSMIC deployed?Then, why is not QoSMIC deployed?

Need for industrial supportNeed for industrial support

Complicated implementationComplicated implementation

Timeliness: too lateTimeliness: too late

The QoS taint: some people are “against” QoSThe QoS taint: some people are “against” QoS

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QoSMIC II: The return of the living QoSMIC II: The return of the living deaddead

QoSMIC as a collection of procedures for QoSMIC as a collection of procedures for searchingsearching• Flood locallyFlood locally• Indirection globallyIndirection globally

Possible applicationsPossible applications• Wireless ad hoc networksWireless ad hoc networks• Peer-to-peer networksPeer-to-peer networks

1 Internet Multicasting Copyright M. Faloutsos Copyright M. Faloutsos.

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