Master thesis presented at UofR, Canada.

1. 1
Optimizing Multicast
p g
Throughput in IP Networks
M. Reza Rahimi,
M. Reza Rahimi
Software Systems Engineering,
University of Regina,
Canada,
September 2009.
September 2009.

2. 2
Outline
O tli
• 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 Networks

3. 3
Introduction
I t d ti
• Data transfer over network could be categorized into
g
three main groups:
• Unicast:
• Transfer of data from one source to one destination (Path
Packing).
Source: Wikipedia
• Broadcast:
• Transfer of data from one source to all of the entire nodes in
the network (Spanning Tree Packing ).
Optimizing Multicast Throughput in IP Networks Source: Wikipedia

4. 4
• 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:
• Maximum amount of information into terminals (Internet).
M i f i f i i i l (I )
• Energy (Wireless Sensor Networks).
• Delay (Internet Telephone).
• Fault Tolerance (specially in wireless networks).
( p y )
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5. 5
• In this thesis we are concerned with the Throughput
Throughput
(optimum data transfer )
(optimum data transfer ) and Quality in Multimedia
Quality Multimedia
Applications.
• Let’s consider the main three mentioned problems in
much more details.
• This will lead us to the following questions:
• Question:
• What is the Maximum Amount of Information that can
What is the Maximum Amount of Information that can
be transferred in each scenario? Is the solution
traceable in reasonable time ?
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6. 6
• Unicast: The answer is yes according to the Maximum‐
Maximum‐
Maximum
Flow Min‐Cut Theorem:
Flow Min‐Cut 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’s Theorem we can
Edmond’s Theorem
find the optimum solution in polynomial time.
• S h
So in this scenario, with routing and duplication h
h routing and duplication the
d d l
optimum value can be reached in polynomial time.
• Multicast: Unfortunately the following result is valid :
Multicast: Unfortunately , the following result is valid :
Optimizing Multicast Throughput in IP Networks

7. 7
• Generally there is no Polynomial Time
Generally there is no
Algorithms to find the optimal Duplicate and
to find the optimal Duplicate
Forward strategy in Multicasting (P≠NP).
strategy in Multicasting (P≠NP).
Optimizing Multicast Throughput in IP Networks

8. 8
Network Coding vs Routing
N t k C di R ti
Optimizing Multicast Throughput in IP Networks

9. 9
• 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 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 CFLow which
CFLow
results the throughput of network coding.
lt th th h t f t k di
• This technique is used to find the upper bound on
g g p
routing throughput
Optimizing Multicast Throughput in IP Networks

10. 10
Thesis Goal
Th i G l
• 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 demands
demands.
• B th d t t
Both data transfer application and multimedia
f li ti d lti di
application are considered.
• 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 small.
duplex communication links ) this gap is small
Optimizing Multicast Throughput in IP Networks

11. 11
• 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 average).
coding upper bound (92% in a erage)
• For multimedia applications rate‐distortion
, y
framework, a metric that is usually used for measuring g
multimedia application performance, is used.
• Through simulation we show that our algorithms
achieve average multimedia multicast qualities that
are only on average, 0.44 db worse than that
achievable with network coding
Optimizing Multicast Throughput in IP Networks

12. 12
Problems Formulation
P bl F l ti
• Let be a weighted undirected graph .
weighted 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 achievable
if there exists a rate assignment function such that:
Optimizing Multicast Throughput in IP Networks

13. 13
• The first constraint is the link capacity constraint
link capacity constraint that
the total flow on each link must not exceed the link
th t t l fl h li k t t d th li k
capacity,
• The second one is the source throughput or demand
throughput demand.
• These constraints are the only constraints when tree
packing is allowed.
Optimizing Multicast Throughput in IP Networks

14. 14
• 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 MaxMin
optimization problem:
Optimizing Multicast Throughput in IP Networks

15. 15
Optimizing Multicast Throughput in IP Networks

16. 16
• Linear Multicast Optimization Problem:
• Different rate priorities to different sources.
• And its network coding version:
Optimizing Multicast Throughput in IP Networks

17. 17
Optimizing Multicast Throughput in IP Networks

18. 18
• Rate Distortion Optimization Problem:
• This frame‐work is suitable for multimedia
applications.
pp
• D is the rate‐distortion function. In this thesis, a power‐
is the rate distortion function. In this thesis, a power
law function will be used.
• The resulting optimization problem when network
coding is allowed:
Optimizing Multicast Throughput in IP Networks

19. 19
Optimizing Multicast Throughput in IP Networks

20. 20
Proposed Tree Packing Algorithms
P d T P ki Al ith
• Three‐Classes of Cooperative Tree‐Packing Algorithms:
Three‐Classes of Cooperative Tree‐
p g g
• Non‐Cooperative Tree Packing Class: In this class each
Non‐Cooperative Tree Packing Class
source packs its own tree independently (Greedy), according to some
strategy (e.g., minimum hop tree, minimum weight tree, maximum
weight Steiner tree).
i h S i )
Optimizing Multicast Throughput in IP Networks

21. 21
Optimizing Multicast Throughput in IP Networks

22. 22
• Medium‐Cooperative Tree Packing Class:
Medium‐
• 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 round.
one tree in that round
• At the end of the each round, we know how
many times each edge has been selected. Thus,
the maximum rate that could be assigned to each
th i t th t ld b i d t h
edge is equal to the capacity of that edge, divided
by the number of times it has been selected
Optimizing Multicast Throughput in IP Networks

23. 23
Optimizing Multicast Throughput in IP Networks

24. 24
• Highly‐Cooperative Tree Packing Class:
Highly‐
• 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
We propose the Cooperative Shortest Path Tree
th C
Cooperative Shortest Path Tree
C ti Sh t t P th T
Packing Algorithm (CSPT
CSPT) for this purpose.
Optimizing Multicast Throughput in IP Networks

25. 25
Optimizing Multicast Throughput in IP Networks

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Optimizing Multicast Throughput in IP Networks

27. 27
Why CSPT works well?
Wh CSPT k ll?
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Optimizing Multicast Throughput in IP Networks

29. 29
Optimizing Multicast Throughput in IP Networks

30. 30
Optimizing Multicast Throughput in IP Networks

31. 31
Implementation On the Standard
Protocols
• Using OSPF as the basic protocol
as the basic protocol.
• OSPF routing protocol is a Link State protocol.
• 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.
• The routers database information are periodically sent
to the entire routers in the network.
Optimizing Multicast Throughput in IP Networks

32. 32
• 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 Backup
Designated Router (BDR)
• The Designated Router tells all the other routers about
changes in the network by sending out Link State
Advertisements (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.
Optimizing Multicast Throughput in IP Networks

33. 33
• Using OSPF , 3 classes of tree packing could be
implemented as follows:
i l d f ll
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34. 34
Some Simulation Results
S Si l ti R lt
• Random graph with 50 nodes uniform link capacity
nodes, uniform link capacity
ranged [1,10] G is considered and network degree from 2
to 10.
• Up to 5 source nodes and total number of 30 terminals
are considered.
Optimizing Multicast Throughput in IP Networks

35. 35
• Simulation Results of the MaxMin Multicast
Optimization Problem :
O i i i P bl
• 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
Optimizing Multicast Throughput in IP Networks

36. 36
• 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 g
throughput:
Optimizing Multicast Throughput in IP Networks

37. 37
Optimizing Multicast Throughput in IP Networks

38. 38
• 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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39. 39
• Simulation Results of the Rate Distortion Optimization
Problem :
Optimizing Multicast Throughput in IP Networks

40. 40
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41. 41
Conclusion and Future Directions
C l i d F t Di ti
• 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 g
CSPT which has the throughput close to the network
coding or in average about 92% of the network coding.
• F lti di applications, CSPT has 0.44db diff
For multimedia li ti CSPT h db difference
with network coding performance
• 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 applications.
coding for multimedia applications
Optimizing Multicast Throughput in IP Networks

42. 42
• 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 Networks