Im phd forum ntu minimum cost multicast routing for multi layered multimedia distribution
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IM PhD Forum, NTU Minimum-Cost Multicast Routing for Multi-Layered Multimedia Distribution. Hsu-Chen Cheng and Frank Yeong-Sung Lin Department of Information Management National Taiwan University [email protected] Outline. Introduction Multirate Multicasting Modified T-M Heuristic

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IM PhD Forum, NTU Minimum-Cost Multicast Routing for Multi-Layered Multimedia Distribution

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Im phd forum ntu minimum cost multicast routing for multi layered multimedia distribution

IM PhD Forum, NTU Minimum-Cost Multicast Routing for Multi-Layered Multimedia Distribution

Hsu-Chen Cheng and Frank Yeong-Sung Lin

Department of Information Management

National Taiwan University

[email protected]


Outline

Outline

  • Introduction

    • Multirate Multicasting

    • Modified T-M Heuristic

  • Two Simple Enhanced Procedures

  • Mathematical Formulation

  • Lagrangean Relaxation

    • Dual Problem

    • Getting Primal Feasible Solutions

  • Computational Experiments

  • With Consideration of Link Capacity Constraint

  • Conclusion and Future Research

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Introduction

Introduction

  • With the popularity of the Internet, multimedia multicasting applications such as e-learning and video conference based on network service are growing rapidly.

  • For Internet service provider, there is one way to achieve the goal of revenue maximization, namely: network planning or traffic engineering.

  • Traffic engineering is the process of controlling how traffic flows through a network in order to optimize resource utilization and network performance.

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Introduction cont

Introduction (cont.)

Cost: 8.5

Cost: 6.5

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Steiner tree problem

Steiner Tree Problem

  • The minimum cost multicast tree problem, which is the Steiner tree problem, is known to be NP-complete.

  • The Steiner tree problem is different to the minimum spanning tree problem in that it permits us to construct, or select, intermediate connection points to reduce the cost of the tree.

  • Heuristics

    • Shortest Path Tree Based (Minimum Depth Tree, MDT)

    • Spanning Tree Based (MST)

    • T-M Heuristic

    • Others (Optimization based)

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Steiner tree heuristics

Steiner Tree Heuristics

S(1) D(2,4)

MDT

Cost: 8

MST

Cost: 9

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T m heuristic

T-M heuristic

  • The T-M heuristic is known to have a solution that is within a factor of two of the optimum.

Cost: 7

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Multirate multicasting

Multirate Multicasting

  • Multi-layered Multicasting (Multirate Multicasting)

    • Taking advantage of recent advances in video encoding and transmission technologies the source only needs to transmit signals that are sufficient for the highest bandwidth destination into a single multicast tree.

  • A multi-layered encoder encodes video data into more than one video stream, including one base layer stream and several enhancement layer streams.

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Multirate multicasting cont

Multirate Multicasting (cont.)

Cost: 8.5

Base layer- 0.5 Mbps

Enhancement layer- 1.5 Mbps

B

B+E

B

B

B+E

B

Cost: 6

Cost: 6.5

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Multirate multicasting cont1

Multirate Multicasting (cont.)

  • For the conventional Steiner tree problem, the link costs in the network are fixed. However, for the minimum cost multi-layered video multicast tree, the link costs are dependent on the set of receivers sharing the link.

  • The heterogeneity of the networks and destinations makes it difficult to design an efficient and flexible mechanism for servicing all multicast group users.

For (i) a given network topology, (ii) the destinations of a multicast group and (iii) the bandwidth requirement of each destination, we attempt to find a feasible routing solution to minimize the cost of a multicast tree for multi-layered multimedia distribution.

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Modified t m heuristic

Modified T-M Heuristic

  • Takahashi and Matsuyama (1980) proposed an approximate algorithm (T-M Heuristic) to deal with the Steiner Tree Problem.

  • Maxemchuk (1997) modifies the T-M Heuristic to deal with the min-cost multicast problem in multi-layered video distribution.

  • Charikar et al. (2004) prove that the problem we discuss here is NP-hard and the result of M-T-M heuristic is no more than 4.214 times of an optimal multicast tree.

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Modified t m heuristic cont

Modified T-M Heuristic (cont.)

  • Separates the receivers into subsets according to the receiving rate.

  • Constructs the multicast for the subset with highest rate by using T-M Heuristic.

  • Using the initial tree, the M-T-M heuristic is then applied to the subsets according the receiving rate from high to low until all destination are included in the tree.

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Example i

Example I

Cost: 5

Cost: 4

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Example ii

Example II

Cost: 11

Cost: 10

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Two enhanced procedures

Two Enhanced Procedures

  • Tie Breaking Procedure

    • When there is a tie, the node with the largest requirement should be selected as the next node to join the tree.

  • Drop and Add Procedure

    1. Compute the number of hops from the source to the destinations.

    2. Sort the nodes in descending order according to {incoming traffic/its own traffic demand}.

    3. In accordance with the order, drop the node and re-add it to the tree. Consider the following possible adding measures and set the best one to be the final tree. Either adds the dropping node to the source node, or to other nodes having the same hop count, or to the nodes having a hop count larger or smaller by one.

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Example

Example

Without TBP

With TBP

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Mathematical formulation

Mathematical Formulation

  • Decision Variables

    • Routing Decision: and

    • Traffic Requirement:

  • Objective Function

  • Capacity Constraint

(2)

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Lagrangean relaxation

LB < Optimal solution < UB

Primal Problem

UB

Adjust u

LB

Lagrangian Relaxation

Problem

subproblem

subproblem

sub-optimal

sub-optimal

Lagrangean Relaxation

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Dual problem

Dual Problem

  • We transform the primal problem (IP) into the Lagrangean Relaxation problem (LR) where Constraints (2) and (6) are relaxed.

  • LR:

  • Subproblem 1:

  • Subproblem 2:

  • Subproblem 3:

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Getting primal feasible solution

Getting Primal Feasible Solution

[Lagrangean based modified T-M heuristic]

Step 1Use as link l’s arc weight and run the M-T-M heuristic.

Step 2After getting a feasible solution, we apply the drop-and-add procedure described earlier to adjust the result.

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Computational experiments

Computational Experiments

  • Testing Networks:

    • Regular networks (Grid Network and Cellular Network)

    • Random networks

    • Scale-free networks

  • For each testing network, several distinct cases which have different pre-determined parameters such as the number of nodes, are considered.

  • The link cost, destination nodes and traffic demands for each destination are generated randomly.

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Computational results

Computational Results

  • Maximum improvement between M-T-M and LR:

    • Grid networks: 16.18%

    • Cellular networks: 23.23 %

    • Random networks: 10.41%

    • Scale-free networks: 11.02 %

  • Solution optimality: 60% of the regular and scale-free networks have a gap of less than 10%, but the result of random networks shown a larger gap.

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Computational results cont

Computational Results (cont.)

  • The M-T-M heuristic performed well in many cases, such as case D of grid network and case D of random network.

  • The tie breaking procedure we proposed is not uniformly better than random selection.

  • The results of experiments shown that the drop and add procedure does reduce the cost of the multicast tree.

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Computational results cont1

Computational Results (cont.)

  • There are two main reasons of which the Lagrangean based heuristic works better than the simple algorithm.

    • First, the simple algorithm routes the group in accordance with fixed link cost and residual capacity merely, whereas the Lagrangean based heuristic makes use of the related Lagrangean multipliers. The Lagrangean multipliers include the potential cost for routing on each link in the topology.

    • Second, the Lagrangean based heuristic is iteration-based and is guaranteed to improve the solution quality iteration by iteration.

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With consideration of link capacity constraint

With Consideration of Link Capacity Constraint

  • Single commodity problem Multi-commodity problem

(1)

(2)

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Multiplier based adjustment procedure lap

Multiplier-based Adjustment Procedure (LAP)

1) Compute the aggregate flow of each link.

2) Sort the links by the difference between aggregate flow of each link and the link capacity in descending order.

3) Choose the first link. If the difference value of the link is positive, go to Step 4, otherwise Step 6.

4) Choose the group, which have the minimal sensitivity value on that link, to drop and use as link l’s arc weight and run the M-T-M heuristic to re-add it to the tree. Consider the following possible adding measures and set the best one to be the final tree. Either adds the dropping node to the source node, or to other nodes having the same hop count, or to the nodes having a hop count larger or smaller by one.

5) If a feasible solution is found, go to Step2, otherwise Step 6.

6) Stop.

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Computational results1

Computational Results

  • Maximum improvement between M-T-M and LR-MTM:

    • Grid networks: 13.46 %

    • Cellular networks: 8.83 %

    • Random networks: 15.4 %

    • Scale-free networks: 10.6 %

  • Solution optimality:72% of the regular and scale-free networks have a gap of less than 10%, but the result of random networks shown a larger gap.

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Conclusion

Conclusion

  • The achievement of this paper can be expressed in terms of mathematical formulation and experiment performance.

  • The model can also be easily extended to deal with the constrained multicast routing problem for multi-layered multimedia distribution by adding QoS constraints.

  • The min-cost model proposed in this paper can be modified as a max-revenue model, with the objective of maximizing total system revenues by totally, or partially, admitting destinations into the system.

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References

References

  • Hsu-Chen Cheng and Frank Yeong-Sung Lin, “Minimum-Cost Multicast Routing for Multi-Layered Multimedia Distribution,” Proc. 7th IFIP/IEEE International Conference on Management of Multimedia and Network Services (MMNS ‘04), Lecture Notes In Computer Science 3271, pp.102-114, October 3- 6, 2004, San Diego, USA.

  • Hsu-Chen Cheng and Frank Yeong-Sung Lin, “A Capacitated Minimum-Cost Multicast Routing Algorithm for Multirate Multimedia Distribution,” Proc. 2004 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS ‘04),  November 18~19, 2004. Seoul, Korea.

  • Hsu-Chen Cheng and Frank Yeong-Sung Lin, “Maximum-Revenue Multicast Routing and Partial Admission Control for Multimedia Distribution,” Proc. International Computer Symposium, Dec. 15-17, 2004, Taipei, Taiwan.

  • Hsu-Chen Cheng and Frank Yeong-Sung Lin, “Maximum-Revenue Multicast Routing and Partial Admission Control for Multirate Multimedia Distribution,” accepted for publication, Proc. The 19th IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA2005).

  • Frank Yeong-Sung Lin, Hsu-Chen Cheng and Jung-Yao Yeh, “A Minimum Cost Multicast Routing Algorithm with the Consideration of Dynamic User Membership,” accepted for publication, Proc. 2005 The International Conference on Information Networking (ICOIN2005),Lecture Notes In Computer Science 3288,Jan. 31- Feb 2, Jeju, Korea.

  • Hsu-Chen Cheng, Chih-Chun Kuo and Frank Yeong-Sung Lin, A Multicast Tree Aggregation Algorithm in Wavelength-routed WDM Networks, Proc. 2004 SPIE Asia-Pacific Optical Communications Conference (APOC ‘04), November 7–11, 2004, Beijing, China. 

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