Optimizing Multicast Throughput in IP Network

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OptimizingOptimizing MulticastMulticastp gp gThroughputThroughput inin IPIP NetworksNetworks

M. Reza RahimiM. Reza Rahimi,

Software Systems Engineering,Software Systems Engineering,

University of Regina,University of Regina,

Canada,Canada,

September September 20092009..

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Outline

• Introduction

• Thesis Goal

• Problems Formulation

• Proposed Tree Packing Algorithms

• Implementation On Standard Protocols

• Some Simulation Results

• Conclusion and Future Directions

Optimizing Multicast Throughput in IP NetworksOptimizing Multicast Throughput in IP Networks

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Introduction

• Data transfer over network could be categorized into gthree main groups:•• UnicastUnicast::

• Transfer of data from one source to one destination (PathTransfer of data from one source to one destination (PathPacking).

•• Broadcast:Broadcast:• Transfer of data from one source to all of the entire nodes in

Source: Wikipedia

Transfer of data from one source to all of the entire nodes in the network (Spanning Tree Packing ).

Optimizing Multicast Throughput in IP NetworksOptimizing Multicast Throughput in IP Networks Source: Wikipedia

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•• Multicast:Multicast:Multicast:Multicast:• Transfer of data from one source to group of destinations

but not all and not one of the nodes (Steiner Trees).

Source: Wikipedia

• Different Optimization Metrics Optimization Metrics can be considered depending on the application:

M i f i f i i i l (I )M i f i f i i i l (I )•• Maximum amount of information into terminals (Internet).Maximum amount of information into terminals (Internet).•• Energy (Wireless Sensor Networks).Energy (Wireless Sensor Networks).•• Delay (Internet Telephone). Delay (Internet Telephone). •• Fault Tolerance (specially in wireless networks).Fault Tolerance (specially in wireless networks).( p y )( p y )


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• In this thesis we are concerned with the ThroughputThroughputIn this thesis we are concerned with the ThroughputThroughput(optimum data transfer ) (optimum data transfer ) and QualityQuality in MultimediaMultimediaApplications.

• Let’s consider the main three mentioned problems in much more details.

• This will lead us to the following questions:• This will lead us to the following questions:• Question:

•• What is the What is the Maximum Amount of Information Maximum Amount of Information that can that can be transferred in each scenario? Is the solution be transferred in each scenario? Is the solution traceable traceable in reasonable time ??


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• Unicast: The answer is yes according to the MaximumMaximum• Unicast: The answer is yes according to the MaximumMaximum‐‐Flow MinFlow Min‐‐Cut TheoremCut Theorem:

We can find the maximum possible information transfer rate with routing (only forwarding information packets) in polynomial time in Unicast Scenario.

• Broadcast: According to the Edmond’sEdmond’s Theorem Theorem we can find the optimum solution in polynomial time.

S h h d d l d d l h • So in this scenario, with routing and duplication routing and duplication the optimum value can be reached in polynomial time.

• Multicast: Unfortunately the following result is valid :Multicast: Unfortunately , the following result is valid :


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•• Generally there is Generally there is nono PolynomialPolynomial TimeTimeAlgorithmsAlgorithms to find the optimal to find the optimal DuplicateDuplicate and and ForwardForward strategy in strategy in Multicasting (P≠NP).Multicasting (P≠NP).


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Network Coding vs Routing


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• This technique is called network coding network coding in literature.

• Network coding contains routing as a special case The optimal network coding throughput is at least as

large as the optimal routing throughputlarge as the optimal routing throughput

• Question: How can we find the best coding scheme and what is the maximum possible throughput?p g p

• The first question is hard to answer in many different cases. But there is a useful technique called CFLowCFLow which

lt th th h t f t k diresults the throughput of network coding.

• This technique is used to find the upper bound on routing throughputg g p


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Thesis Goal

• We consider the generalized version of multicast when • We consider the generalized version of multicast when there are Several Sources Several Sources in the network.

• We consider many different fairness criteria, for y ,example optimizing the minimum throughput among sources and sources with unequal demandsdemands.

B th d t t f li ti d lti di • Both data transfer application and multimedia application are considered.

• Although network coding could increase the Although network coding could increase the throughput of some network dramatically, it has been shown that for Undirected Networks (network with full Undirected Networks (network with full duplex communication links ) duplex communication links ) this gap is smallduplex communication links ) duplex communication links ) this gap is small.


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• We proposed tree multicast routing optimization • We proposed tree multicast routing optimization algorithms based on standard IP routing protocols, that can achieve throughputs very close to the network coding upper bound (92% in a erage)coding upper bound (92% in average).

• For multimedia applications rate‐distortionframework, a metric that is usually used for measuring , y gmultimedia application performance, is used.• Through simulation we show that our algorithms

achieve average multimedia multicast qualities that achieve average multimedia multicast qualities that are only on average, 0.44 db worse than that achievable with network coding


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Problems Formulation

• Let be a weighted undirected graphweighted undirected graph• Let be a weighted undirected graphweighted undirected graph .

• Now define to be a set of source nodes , where | | is the set cardinality operator. | | y p

• Let be the set of sink nodes of source Si.

• as the set of all Steiner trees rooted at Si that have as the terminal points .

• A demand vector is said to be achievableif there exists a rate assignment function such that:if there exists a rate assignment function such that:


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• The first constraint is the link capacity constraint link capacity constraint that th t t l fl h li k t t d th li k the total flow on each link must not exceed the link capacity,

• The second one is the source throughputthroughput or demanddemand.The second one is the source throughputthroughput or demanddemand.

• These constraints are the only constraints when tree packing is allowed.


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•• MaxMin Multicast MaxMin Multicast optimization problem:

• When the goal is to maximize the minimum demand of the source nodes:

• If network coding is allowed, using Cflow technique, the following program finds the upper bound on MaxMinoptimization problem: optimization problem:


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• Linear Multicast Optimization Problem:• Different rate priorities to different sources.

• And its network coding version:


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• Rate Distortion Optimization Problem:• Rate Distortion Optimization Problem:

• This frame‐work is suitable for multimedia applications.pp

• D is the rate‐distortion function. In this thesis, a power‐D is the rate distortion function. In this thesis, a powerlaw function will be used.

• The resulting optimization problem when network coding is allowed:


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Proposed Tree Packing Algorithms

•• ThreeThree‐‐Classes of Cooperative TreeClasses of Cooperative Tree‐‐Packing Algorithms:Packing Algorithms:pp g gg g•• NonNon‐‐Cooperative Tree Packing ClassCooperative Tree Packing Class: In this class each

source packs its own tree independently (Greedy), according to some strategy (e.g., minimum hop tree, minimum weight tree, maximum

i h S i )weight Steiner tree).


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•• MediumMedium‐‐Cooperative Tree Packing Class: Cooperative Tree Packing Class: • Round‐robin family of tree packing algorithms.• In each round of the algorithm, each source selects one tree according to its own strategy. g gy

• The rate assignment is postponed to the end of each round, i.e., when each source has nominated one tree in that roundone tree in that round.

• At the end of the each round, we know how many times each edge has been selected. Thus, th i t th t ld b i d t h the maximum rate that could be assigned to each edge is equal to the capacity of that edge, divided by the number of times it has been selected


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•• HighlyHighly Cooperative Tree Packing Class:Cooperative Tree Packing Class:•• HighlyHighly‐‐Cooperative Tree Packing Class:Cooperative Tree Packing Class:• the Rate assignment is postponed until the very last

round.• Sources pack one tree at each round, according to

their own strategy. At the end of all the rounds, rates are assigned to the trees.g

• This class of algorithms is our main proposed solution to the distributed tree packing problem.W th C ti Sh t t P th T C ti Sh t t P th T • We propose the Cooperative Shortest Path Tree Cooperative Shortest Path Tree Packing AlgorithmPacking Algorithm (CSPTCSPT) for this purpose.


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Wh CSPT k ll?

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Why CSPT works well?


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Implementation On the Standard

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Implementation On the Standard Protocols

• Using OSPF as the basic protocol• Using OSPF as the basic protocol.

• OSPF routing protocol is a Link State protocol.

• It is based on cost rather than hops (hops could be • It is based on cost rather than hops (hops could be considered as the special case of cost).

• All the OSPF routers has the Link State Database (LSDB) and executes Dijkstra's algorithm on their database to calculate a shortest path route to a given destination node from the current router.destination node from the current router.

• The routers database information are periodically sent to the entire routers in the network.


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• In OSPF, two important multicast addresses are used.

• When an OSPF area is started, one router is elected the Designated Router (DR), and another as the BackupDesignated Router (BDR)Designated Router (BDR)

• The Designated Router tells all the other routers about changes in the network by sending out Link StateAdvertisements (LSA) on multicast address 224.0.0.5.

• Every change in the network topology will be send by the OSPF routers on multicast address 224 0 0 6 the OSPF routers on multicast address 224.0.0.6, reserved for the DR and BDR.


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• Using OSPF , 3 classes of tree packing could be i l d f llimplemented as follows:


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Some Simulation Results

• Random graph with 50 nodes uniform link capacity Random graph with 50 nodes, uniform link capacity ranged [1,10] G is considered and network degree from 2to 10.

• Up to 5 source nodes and total number of 30 terminals are considered.


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•• Simulation Results of the MaxMin Multicast Simulation Results of the MaxMin Multicast O i i i P bl O i i i P bl Optimization Problem :Optimization Problem :

• The throughput is defined as:

• The following averaged results obtained:CSPT Cooperative Shortest

Path Tree

NCSTEIN NON-COOPEARIVE STEINER

MCSTEIN MEDIUM –COOPERTIVE STEINER

MCDIJK MEDIUM-COOPERTIVE DIJKSTRA

MCBFS MEDIUM-COOPERTIVE BFS

NCBFS NON-COOPEARIVE STEINER

NCDIJK NON-COOPERATIVE DIJKSTRA


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• Convergence Speed: • The relation of the average number of trees and the

average percentage of the CSPT, NCSTEINE and MCSTEINE throughput to the network coding g p gthroughput:


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• Linear Multicast Optimization Problem:

• It reaches on average to the 90% of network coding.

• The following shows the examples with some priority vector.


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•• Simulation Results of the Rate Distortion Optimization Simulation Results of the Rate Distortion Optimization •• Simulation Results of the Rate Distortion Optimization Simulation Results of the Rate Distortion Optimization Problem :Problem :


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Conclusion and Future Directions

• In this thesis the problem of IP multicast in multi• In this thesis the problem of IP multicast in multi‐source environments is discussed and formulated.

• We have proposed a novel tree packing algorithm called p p p g gCSPT which has the throughput close to the network coding or in average about 92% of the network coding.

F lti di li ti CSPT h db diff • For multimedia applications, CSPT has 0.44db difference with network coding performance

• With packing 8 trees per source using CSPT, one could With packing 8 trees per source using CSPT, one could reach the throughput of 50% of network coding for data transfer applications and 3.27db different with network coding for multimedia applicationscoding for multimedia applications.


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• There are some issued that should be considered:• There are some issued that should be considered:• Directect network should be studied.• Noisy network should be considered.• For multimedia application, correlated source should

be considered.


Optimizing Multicast Throughput in IP Network

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