Communication-Efficient (Weighted) Reservoir Sampling from Fully Distributed Data Streams
Data Structures and Algorithms
2020-02-26 v3 Distributed, Parallel, and Cluster Computing
Abstract
We consider communication-efficient weighted and unweighted (uniform) random sampling from distributed data streams presented as a sequence of mini-batches of items. This is a natural model for distributed streaming computation, and our goal is to showcase its usefulness. We present and analyze fully distributed, communication-efficient algorithms for both versions of the problem. An experimental evaluation of weighted reservoir sampling on up to 256 nodes (5120 processors) shows good speedups, while theoretical analysis promises further scaling to much larger machines.
Cite
@article{arxiv.1910.11069,
title = {Communication-Efficient (Weighted) Reservoir Sampling from Fully Distributed Data Streams},
author = {Lorenz Hübschle-Schneider and Peter Sanders},
journal= {arXiv preprint arXiv:1910.11069},
year = {2020}
}
Comments
A previous version of this paper was titled "Communication-Efficient (Weighted) Reservoir Sampling"