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

Cheng, Hsu-ChenNational Taiwan University2 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

Cheng, Hsu-ChenNational Taiwan University3 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.

Cheng, Hsu-ChenNational Taiwan University4 Introduction (cont.) Cost: 8.5 Cost: 6.5

Cheng, Hsu-ChenNational Taiwan University5 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)

Cheng, Hsu-ChenNational Taiwan University6 Steiner Tree Heuristics MDT MST Cost: 8 Cost: 9 S(1) D(2,4)

Cheng, Hsu-ChenNational Taiwan University7 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

Cheng, Hsu-ChenNational Taiwan University8 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.

Cheng, Hsu-ChenNational Taiwan University9 Multirate Multicasting (cont.) B+E B B B B Base layer- 0.5 Mbps Enhancement layer- 1.5 Mbps Cost: 8.5 Cost: 6.5 Cost: 6

Cheng, Hsu-ChenNational Taiwan University10 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.

Cheng, Hsu-ChenNational Taiwan University11 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 times of an optimal multicast tree.

Cheng, Hsu-ChenNational Taiwan University12 1Separates the receivers into subsets according to the receiving rate. 2Constructs the multicast for the subset with highest rate by using T-M Heuristic. 3Using 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. Modified T-M Heuristic (cont.)

Cheng, Hsu-ChenNational Taiwan University13 Example I Cost: 5Cost: 4

Cheng, Hsu-ChenNational Taiwan University14 Example II Cost: 11Cost: 10

Cheng, Hsu-ChenNational Taiwan University15 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.

Cheng, Hsu-ChenNational Taiwan University16 Example Without TBP With TBP

Cheng, Hsu-ChenNational Taiwan University17 Mathematical Formulation Decision Variables  Routing Decision: and  Traffic Requirement: Objective Function Capacity Constraint (2)

Cheng, Hsu-ChenNational Taiwan University18 Lagrangean Relaxation Lagrangian Relaxation Problem Primal Problem sub-optimal subproblem sub-optimal LB < Optimal solution < UB LB Adjust u UB

Cheng, Hsu-ChenNational Taiwan University19 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:

Cheng, Hsu-ChenNational Taiwan University20 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.

Cheng, Hsu-ChenNational Taiwan University21 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.

Cheng, Hsu-ChenNational Taiwan University22 Computational Results Maximum improvement between M-T-M and LR:  Grid networks: 16.18%  Cellular networks: %  Random networks: 10.41%  Scale-free networks: % 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.

Cheng, Hsu-ChenNational Taiwan University23 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. H %11.79% D %0.00% Case Dest #M-T-M TBAD UB LBGAPImp.

Cheng, Hsu-ChenNational Taiwan University24 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.

Cheng, Hsu-ChenNational Taiwan University25 With Consideration of Link Capacity Constraint Single commodity problem  Multi-commodity problem (1) (2)

Cheng, Hsu-ChenNational Taiwan University26 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.

Cheng, Hsu-ChenNational Taiwan University27 Computational Results Maximum improvement between M-T-M and LR-MTM:  Grid networks: %  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.

Cheng, Hsu-ChenNational Taiwan University28 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.

Cheng, Hsu-ChenNational Taiwan University29 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 , 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 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS ‘04), November 18~19, 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 , 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 The International Conference on Information Networking (ICOIN 2005), 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 SPIE Asia- Pacific Optical Communications Conference (APOC ‘04), November 7–11, 2004, Beijing, China.

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