Multiple multicast tree allocation in IP network

Lee, Chae Y.; Cho, Hee K.

doi:10.1016/s0305-0548(03)00068-6

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…A similar problem was studied for wireless networks [34], but the algorithms and their evaluation are mostly focused on specific properties of the wireless transmission. As long as uncertainty or per-consumer adaptability are not considered, the multigroup multicast algorithms [25], [26] are usually evaluated up to 100 or 200 nodes in the network. Our algorithm was shown to work on bigger instances, supporting uncertain network capacities, per-consumer adaptability, and two-level network representation.…”

Section: Discussionmentioning

confidence: 99%

“…However, the MMDOM is not oriented on multimedia streaming and it considers neither typical QoS constraints nor multiple tree building. The MMTA algorithm was proposed for delay bounded multicast routing with multiple groups [25]. Computational intelligence methods became popular for the multiple groups multicast routing as well [26].…”

Section: B Related Problemsmentioning

confidence: 99%

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Media Streams Planning with Uncertain Link Capacities

Troubil

Rudová

Holub

2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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Interactive collaborative environments built upon high quality video applications require minimum capture-todisplay latency to maintain feeling of natural communication. In practice, end users often encounter interruptions due to network congestion, which they could not avoid without knowledge of physical network infrastructure. We solve the problem of application level multipoint routing without assuming accurate knowledge of network link capacities, but rather relying on their estimates by network inference and bandwidth measurement tools. Our approach can handle uncertainties caused by both unreliable bandwidth measurements and unexpected capacity sharing. Support for on-the-fly transcoding of data streams on multicast agents allows per-recipient adaptation of video quality and bandwidth. We have implemented an ant colony optimization algorithm in the CoUniverse middleware for orchestration of collaborative environments. Experiments show very good quality of results on most practical use cases and ability to solve significantly larger instances than optimal approaches based on mixed integer programming.

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Section: Discussionmentioning

confidence: 99%

Section: B Related Problemsmentioning

confidence: 99%

Media Streams Planning with Uncertain Link Capacities

Troubil

Rudová

Holub

2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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“…They assume that each multicast tree requires the same amount of bandwidth and devotes to minimize the maximum congestion. Literature analyzed the similar problem while each multicast tree has different bandwidth requirement. In addition, some researchers reconsider the tree packing problem under different optimization objectives.…”

Section: Related Workmentioning

confidence: 99%

The packing problem of uncertain multicasts

Ren

Guo

Xie

et al. 2016

Concurrency and Computation

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Multicast performs better than unicast in delivering the same content from a fixed single source to a set of destinations. Many efforts have been made to optimize such kind of deterministic multicast, such as minimizing the transmission cost of each multicast session. In practice, it is not necessary that the source of each multicast session has to be in a specific location, as long as certain constraints are satisfied. Accordingly, applications usually meet a novel multicast with uncertain sources, ie, uncertain multicast. That is, multiple nodes have the responsibility to act as the root node of a multicast session. Prior proposals have addressed an uncertain multicast by constructing the minimum cost forest. However, it is still unknown how to efficiently share the network resources, when a set of uncertain multicast occupies the network simultaneously. To tackle such a challenging issue, we present the packing problem of uncertain multicasts (MPU) to minimize the total transmission cost, under the constraint of link capacity. We prove that the MPU problem is NP-hard. An intrinsic solution is constructing the minimum cost forest for each uncertain multicast individually. This method, however, is inefficient and may be infeasible because of the constraint of link capacity. Thus, we design 2 dedicated greedy methods, named priority-based and adjusting congested link, to approximate the optimal solution. The comprehensive results indicate that both of our 2 methods can find a feasible solution for the MPU problem. Moreover, given a set of uncertain multicasts, the adjusting congested link method can generate a desired transmission structure for each uncertain multicast and achieve the least total cost when packing them. KEYWORDS packing problem, routing algorithm, uncertain multicast 1 Concurrency Computat: Pract Exper. 2017;29:e3985.wileyonlinelibrary.com/journal/cpe

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“…Ravindran et al [7] developed a model and provided heuristics for the problem of merging two or more tree to minimize overall bandwidth consumption. Chen et al [8] and Lee and Cho [9] developed IP models together with heuristics to merge multicasting trees for maximizing the minimum residual bandwidth. While these researches attempt to construct and pack the trees in the network, for the problem we consider, we are given a set of multicast trees (for different sets of channels) and our goal is to find a way to distribute data throughout the trees while optimizing zap time and bandwidth.…”

Section: Related Workmentioning

confidence: 99%

Multi-source IPTV networks: Zap time and bandwidth optimization

Bailey

Chen

Radhakrishnan

et al. 2013

2013 International Conference on Computing, Networking and Communications (ICNC)

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Broadcast television viewing over the Internet (IPTV) is becoming commonplace. Multicasting trees serve as an efficient mechanism to deliver streaming data as each internal node duplicates the packets it receives and sends it along to its children which eventually delivers them to the clients. Given a set of multicasting trees whose roots are servers capable of broadcasting a set of distinct channels, and a set of clients (which are not part of the multicasting trees) each with a set of requested channels, our goal is to determine for each client for each of its channel request, a node (contact node) in the appropriate multicast tree (that serves the channel). The contact nodes are determined in such a way that certain optimization constraints are taken into consideration and satisfied. We have provided Integer Programming (IP) models and heuristics to find these contact nodes in order to optimize constraints on zap time and bandwidth utilization. The proposed IP model is novel and the polynomial-time heuristic provide a fairly good solution in a short amount of time.

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Multiple multicast tree allocation in IP network

Cited by 12 publications

References 12 publications

Media Streams Planning with Uncertain Link Capacities

Media Streams Planning with Uncertain Link Capacities

The packing problem of uncertain multicasts

Multi-source IPTV networks: Zap time and bandwidth optimization

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