Multicast routing for multimedia communication
IEEE/ACM Transactions on Networking (TON)
Communications of the ACM
An iterative algorithm for delay-constrained minimum-cost multicasting
IEEE/ACM Transactions on Networking (TON)
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Enabling conferencing applications on the internet using an overlay muilticast architecture
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Introduction to Algorithms
QDMR: An Efficient QoS Dependent Multicast Routing Algorithm
RTAS '99 Proceedings of the Fifth IEEE Real-Time Technology and Applications Symposium
ICOIN '01 Proceedings of the The 15th International Conference on Information Networking
Application Deployment in Virtual Networks Using the X-Bone
DANCE '02 Proceedings of the 2002 DARPA Active Networks Conference and Exposition
Enhancement of the CBT Multicast Routing Protocol
ICPADS '01 Proceedings of the Eighth International Conference on Parallel and Distributed Systems
Overlay Multicast Trees of Minimal Delay
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
An effective heuristic algorithm for dynamic multicast routing with delay-constrained
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Approximation and heuristic algorithms for minimum-delay application-layer multicast trees
IEEE/ACM Transactions on Networking (TON)
OMNI: An efficient overlay multicast infrastructure for real-time applications
Computer Networks: The International Journal of Computer and Telecommunications Networking - Overlay distribution structures and their applications
IEEE Communications Magazine
Evaluation of multicast routing algorithms for real-time communication on high-speed networks
IEEE Journal on Selected Areas in Communications
Multicast routing with end-to-end delay and delay variation constraints
IEEE Journal on Selected Areas in Communications
Destination-driven routing for low-cost multicast
IEEE Journal on Selected Areas in Communications
Multicast routing and bandwidth dimensioning in overlay networks
IEEE Journal on Selected Areas in Communications
Multi-stream synchronization for 3D tele-immersive and collaborative environment
Proceedings of the 2nd International Conference on Immersive Telecommunications
DVBMN-l: delay variation bounded multicast network with multiple paths
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Overlay multicast routing algorithm with delay and delay variation constraints
APPT'07 Proceedings of the 7th international conference on Advanced parallel processing technologies
Journal of Systems and Software
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Computer supported collaborative applications on overlay networks are gaining popularity among users who are geographically dispersed. Examples of these kinds of applications include video-conferencing, distributed database replication, and online games. This type of application requires a multicasting subnetwork, using which messages should arrive at the destinations within a specified delay bound. These applications also require that destinations receive the message from the source at approximately the same time. The problem of finding a multicasting subnetwork with delay and delay-variation bound has been proved to be an NP Complete problem in the literature and heuristics have been proposed for this problem. In this paper, we provide an efficient heuristic to obtain a multicast subnetwork on an overlay network, given a source and a set of destinations that is within a specified maximum delay and a specified maximum variation in the delays from a source to the destinations. The time-complexity of our algorithm is O(|E| + nk \log(|E|/n) + m^{2}k), where n and |E| are the number of nodes and edges in the network, respectively, k is the number of shortest paths determined, and m is the number of destinations. We have shown that our algorithm is significantly better in terms of time-complexity than existing algorithms for the same problem. Our extensive empirical studies indicate that our heuristic uses significantly less runtime in comparison with the best-known heuristics while achieving the tightest delay variation for a given end-to-end delay bound.