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.
The conventional wisdom has been that Internet protocol (IP) is the natural protocol layer for implementing multicast related functionality. However, more than a decade after its initial proposal, IP multicast is still plagued with concerns pertaining to scalability, network management, deployment, and support for higher layer functionality such as error, flow, and congestion control. We explore an alternative architecture that we term end system multicast, where end systems implement all multicast related functionality including membership management and packet replication. This shifting of multicast support from routers to end systems has the potential to address most problems associated with IP multicast. However, the key concern is the performance penalty associated with such a model. In particular, end system multicast introduces duplicate packets on physical links and incurs larger end-to-end delays than IP multicast. We study these performance concerns in the context of the Narada protocol. In Narada, end systems self-organize into an overlay structure using a fully distributed protocol. Further, end systems attempt to optimize the efficiency of the overlay by adapting to network dynamics and by considering application level performance. We present details of Narada and evaluate it using both simulation and Internet experiments. Our results indicate that the performance penalties are low both from the application and the network perspectives. We believe the potential benefits of transferring multicast functionality from end systems to routers significantly outweigh the performance penalty incurred.  We study decentralised low delay degree-constrained overlay multicast tree construction for single source real-time applications. This optimisation problem is NP-hard even if computed centrally. We identify two problems in traditional distributed solutions, namely the greedy problem and delay-cost trade-off. By offering solutions to these problems, we propose a new self-organising distributed tree building protocol called MeshTree. The main idea is to embed the delivery tree in a degree-bounded mesh containing many low cost links. Our simulation results show that MeshTree is comparable to the centralised Compact Tree algorithm, and always outperforms existing distributed solutions in delay optimisation. In addition, it always yields trees with lower cost and traffic redundancy.