Recent proposals in multicast overlay construction have demonstrated the importance of exploiting underlying network topology. However, these topology-aware proposals often rely on incremental and periodic refinements to improve the system performance. These approaches are therefore neither scalable, as they induce high communication cost due to refinement overhead, nor efficient because long convergence time is necessary to obtain a stabilized structure. In this paper, we propose a highly scalable locating algorithm that gradually directs newcomers to their a set of their closest nodes without inducing high overhead. On the basis of this locating process, we build a robust and scalable topology-aware clustered hierarchical overlay scheme, called LCC. We conducted both simulations and PlanetLab experiments to evaluate the performance of LCC. Results show that the locating process entails modest resources in terms of time and bandwidth. Moreover, LCC demonstrates promising performance to support large scale multicast applications.
In the overlay-router approach such as OMNI @cite_5 and TOMA @cite_13 , reliable servers are installed across the network to act as application-level multicast routers. The content is transmitted from the source to a set of receivers on a multicast tree consisting of the overlay servers. This approach is designed to be scalable since the receivers get the content from the application-level routers, thus alleviating bandwidth demand at the source. However, it needs dedicated infrastructure deployment and costly servers.
@cite_5 @cite_13
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This paper presents an overlay architecture where service providers deploy a set of service nodes (called MSNs) in the network to efficiently implement media-streaming applications. These MSNs are organized into an overlay and act as application-layer multicast forwarding entities for a set of clients. We present a decentralized scheme that organizes the MSNs into an appropriate overlay structure that is particularly beneficial for real-time applications. We formulate our optimization criterion as a "degree-constrained minimum average-latency problem" which is known to be NP-hard. A key feature of this formulation is that it gives a dynamic priority to different MSNs based on the size of its service set. Our proposed approach iteratively modifies the overlay tree using localized transformations to adapt with changing distribution of MSNs, clients, as well as network conditions. We show that a centralized greedy approach to this problem does not perform quite as well, while our distributed iterative scheme efficiently converges to near-optimal solutions. In this paper, we propose a Two-tier Overlay Multicast Architecture (TOMA) to provide scalable and efficient multicast support for various group communication applications. In TOMA, Multicast Service Overlay Network (MSON) is advocated as the backbone service domain, while end users in the access domains form a number of small clusters, in which an application-layer multicast protocol is used for the communication between the clustered end users. TOMA is able to provide efficient resource utilization with less control overhead, especially for large-scale applications. It also alleviates the state scalability problem and simplifies multicast tree construction and maintenance when there are large numbers of groups in the networks. Simulation studies are conducted and the results demonstrate the promising performance of TOMA.