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.
We propose an application level multicast approach, Topology Aware Grouping (TAG), which exploits underlying network topology information to build efficient overlay networks among multicast group members. TAG uses information about path overlap among members to construct a tree that reduces the overlay relative delay penalty, and reduces the number of duplicate copies of a packet on the same link. We study the properties of TAG, and model and experiment with its economies of scale factor to quantify its benefits compared to unicast and IP multicast. We also compare the TAG approach with the ESM approach in a variety of simulation configurations including a number of real Internet topologies and generated topologies. Our results indicate the effectiveness of the algorithm in reducing delays and duplicate packets, with reasonable algorithm time and space complexities. We describe a new scalable application-layer multicast protocol, specifically designed for low-bandwidth, data streaming applications with large receiver sets. Our scheme is based upon a hierarchical clustering of the application-layer multicast peers and can support a number of different data delivery trees with desirable properties.We present extensive simulations of both our protocol and the Narada application-layer multicast protocol over Internet-like topologies. Our results show that for groups of size 32 or more, our protocol has lower link stress (by about 25 ), improved or similar end-to-end latencies and similar failure recovery properties. More importantly, it is able to achieve these results by using orders of magnitude lower control traffic.Finally, we present results from our wide-area testbed in which we experimented with 32-100 member groups distributed over 8 different sites. In our experiments, average group members established and maintained low-latency paths and incurred a maximum packet loss rate of less than 1 as members randomly joined and left the multicast group. The average control overhead during our experiments was less than 1 Kbps for groups of size 100. With the rise of peer-to-peer networks, two problems have become prominent: (1) significant network traffic among peers, to probe the latency among those peers to improve lookup performance; (2) the need for increased information flow across protocol layer boundaries, to allow for cross-layer adaptations. The particular work here focuses on improvements in structured peer-to-peer networks based on autonomous system information. We find that by using autonomous system information effectively we can achieve lookup performance approaching that based on proximity neighbor selection, but with much less network traffic. We also demonstrate improvements in replication in structured peer-to-peer networks using AS topology and scoping information. Finally, we review this approach in the context of network architecture 