Almost-Optimal Gossip-Based Aggregate Computation

Motivated by applications to modern networking technologies, there has been interest in designing efficient gossip-based protocols for computing aggregate functions. While gossip-based protocols provide robustness due to their randomized nature, reducing the message and time complexity of these protocols is also of paramount importance in the context of resource-constrained networks such as sensor and peer-to-peer networks. We present provably time-optimal efficient gossip-based algorithms for aggregate computation with almost optimal message complexity. Given an n-node network, our algorithms guarantee that all the nodes can compute the common aggregates (such as Max, Min, Average, Sum, and Count) of their values in optimal $O(\log n)$ time and using $O(n \log \log n)$ messages. Our result improves on the algorithm of Kempe, Dobra, and Gehrke [Proceedings of the IEEE Annual Symposium on Foundations of Computer Science, 2003, pp. 482–491] that is time-optimal but uses $O(n \log n)$ messages, as well as on...

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