Optimal multicast route packing

This paper considers the hop-constrained multicast route packing problem with a bandwidth reservation to build QoS-guaranteed multicast routing trees with a minimum installation cost. Given a set of multicast sessions, each of which has a hop limit constraint and a bandwidth requirement, the problem...

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Bibliographic Details
Published in:European journal of operational research Vol. 196; no. 1; pp. 351 - 359
Main Authors: Kang, Jangha, Park, Kyungchul, Park, Sungsoo
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.07.2009
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Algorithms
Applied sciences
Bandwidths
Branch & bound algorithms
Column generation
Cutting plane
Exact sciences and technology
Flows in networks. Combinatorial problems
Integer programming
Integer programming Multicast Optimization Column generation Cutting plane
Multicast
Operational research and scientific management
Operational research. Management science
Optimization
Packing problem
Routing
Studies
Integer programming
Multicast
Column generation
Cutting plane
Optimization
Tree(graph)
Branch and bound method
Bin packing problem
Routing
Cutting plane method
Branch and cut method
Bandwidth
Capacity constraint
Service quality
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:This paper considers the hop-constrained multicast route packing problem with a bandwidth reservation to build QoS-guaranteed multicast routing trees with a minimum installation cost. Given a set of multicast sessions, each of which has a hop limit constraint and a bandwidth requirement, the problem is to determine the set of multicast routing trees in an arc-capacitated network with the objective of minimizing the cost. For the problem, we propose a branch-and-cut-and-price algorithm, which can be viewed as a branch-and-bound method incorporating both the strong cutting plane algorithm and the column generation method. We implemented and tested the proposed algorithm on randomly generated problem instances with sizes up to 30 nodes, 570 arcs, and 10 multicast sessions. The test results show that the algorithm can obtain the optimal solution to practically sized problem instances within a reasonable time limit in most cases.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2008.03.006