Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Euro-Par'12 Proceedings of the 18th international conference on Parallel processing workshops
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Grid applications often need to distribute large amounts of data efficiently from one cluster to multiple others (multicast). Existing sender-initiated methods arrange nodes in optimized tree structures, based on external network monitoring data. This dependence on monitoring data severely impacts both ease of deployment and adaptivity to dynamically changing network conditions. In this paper, we present Robber, a collective, receiver-initiated, high-throughput multicast approach inspired by the BitTorrent protocol. Unlike BitTorrent, Robber is specifically designed to maximize the throughput between multiple cluster computers. Nodes in the same cluster work together as a collective that tries to steal data from peer clusters. Instead of using potentially outdated monitoring data, Robber automatically adapts to the currently achievable bandwidth ratios. Within a collective, nodes automatically tune the amount of data they steal remotely to their relative performance. Our experimental evaluation compares Robber to BitTorrent, to Balanced Multicasting, and to its predecessor MOB. Balanced Multicasting optimizes multicast trees based on external monitoring data, while MOB uses collective, receiver-initiated multicast with static load balancing. We show that both Robber and MOB outperform BitTorrent. They are competitive with Balanced Multicasting as long as the network bandwidth remains stable, and outperform it by wide margins when bandwidth changes dynamically. In large environments and heterogeneous clusters, Robber outperforms MOB.