Lightweight reliable overlay multicasting in large-scale P2P networks
Proceedings of the Third C* Conference on Computer Science and Software Engineering
A case for content distribution in peer-to-peer networks
AMT'10 Proceedings of the 6th international conference on Active media technology
Towards microeconomic resources allocation in overlay networks
AMT'10 Proceedings of the 6th international conference on Active media technology
A non-strategic microeconomic model for single-service multi-rate application layer multicast
ICICA'10 Proceedings of the First international conference on Information computing and applications
An economic case for end system multicast
FIS'10 Proceedings of the Third future internet conference on Future internet
Journal of Network and Systems Management
Toward microeconomic allocation of resources in multi-service overlay networks
Journal of Computer and Systems Sciences International
Efficient content-based routing with network topology inference
Proceedings of the 7th ACM international conference on Distributed event-based systems
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In a peer-to-peer overlay network, the phenomenon of multiple overlay links sharing bottleneck physical links leads to correlation of overlay link capacities. We are able to more accurately model the overlay by incorporating these linear capacity constraints (LCCs). We formulate the problem of maximizing bandwidth in overlay multicast using our LCC model. We show that finding a maximum bandwidth multicast tree in an overlay network with LCC is NP-complete. Therefore, an efficient heuristics algorithm is designed to solve the problem. Extensive simulations show that our algorithm is able to construct multicast trees that are optimal or extremely close to optimal, with significantly higher bandwidth than trees formed in overlays with no LCC. Furthermore, we develop a fully distributed algorithm for obtaining near-optimal multicast trees, by means of gossip-based algorithms and a restricted but inherently distributed class of LCC (node-based LCC). We demonstrate that the distributed algorithm converges quickly to the centralized optimal and is highly scalable.