Gossip-based broadcast algorithms, a family of probabilistic broadcast algorithms, trade reliability guarantees against "scalability" properties. Scalability in this context has usually been expressed in terms of message throughput and delivery latency, but there has been little work on how to reduce the memory consumption for membership management and message buffering at large scale. This paper presents lightweight probabilistic broadcast (lpbcast), a novel gossip-based broadcast algorithm, which complements the inherent throughput scalability of traditional probabilistic broadcast algorithms with a scalable memory management technique. Our algorithm is completely decentralized and based only on local information: in particular, every process only knows a fixed subset of processes in the system and only buffers fixed "most suitable" subsets of messages. We analyze our broadcast algorithm stochastically and compare the analytical results both with simulations and concrete implementation measurements.
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