A Modeling Framework for Gossip-based Information Spread

We present an analytical framework for gossip protocols based on the pairwise information exchange between interacting nodes. This framework allows for studying the impact of protocol parameters on the performance of the protocol. Previously, gossip-based information dissemination protocols have been analyzed under the assumption of perfect, lossless communication channels. We extend our framework for the analysis of networks with lossy channels. We show how the presence of message loss, coupled with specific topology configurations,impacts the expected behavior of the protocol. We validate the obtained models against simulations for two protocols.

💡 Research Summary

The paper introduces an analytical framework for gossip‑based information dissemination that models the pairwise interaction between two nodes as a state transition system. Each node’s cache is represented by a binary indicator for the presence of a particular item, yielding four possible joint states (00, 01, 10, 11). Transition probabilities are decomposed into two fundamental quantities: P_select, the probability that a node selects the item for transmission, and P_drop, the probability that an item is evicted from the cache after the exchange. P_select depends only on the exchange buffer size s and cache size c (P_select = s/c), while P_drop depends on the overlap k between the sender’s buffer and the receiver’s cache; under uniform sampling k ≈ s·c/n, giving P_drop ≈ 1 − c/(c+s−k).

Using this building block, the authors derive complete transition matrices for two representative gossip protocols: Newscast (a push‑pull protocol) and Shuffle (used for membership management). For Newscast, they show how the probabilities change when messages can be lost. In fully connected graphs, loss has negligible effect on the uniform distribution of items, but in sparse topologies the loss amplifies correlations between neighboring caches and slows spread dramatically. For Shuffle, similar analysis reveals the impact of loss on view uniformity and convergence speed.

The framework is validated through extensive round‑based simulations (PeerSim‑style) that match the theoretical predictions for both protocols under various loss rates and topologies. The authors also discuss extending the approach to other gossip‑based systems such as Cyclon, indicating that the same pairwise model can capture membership view dynamics.

Overall, the contribution is a versatile, mathematically tractable model that bridges the gap between idealized lossless analyses and realistic networks with unreliable channels. It enables designers to predict performance metrics (spread speed, replication count, view uniformity), optimize protocol parameters, and conduct fast event‑driven simulations without resorting to full system‑level Markov chains. Future work is suggested on asynchronous gossip, dynamic topologies, and multi‑item exchange strategies.