Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks

TL;DR

This paper demonstrates that randomized algorithms significantly reduce communication costs in distributed tracking problems like count, frequency, and rank tracking, compared to deterministic methods, with simple algorithms and strong lower bounds.

Contribution

The paper introduces randomized algorithms that lower communication complexity for distributed count, frequency, and rank tracking, surpassing deterministic approaches with simple, space-efficient solutions.

Findings

Randomized algorithms reduce communication complexity from Θ(k/ε) to Θ(√k/ε).

Algorithms are simple and use only O(1) space per player.

Lower bounds show the optimality of the randomized approach.

Abstract

We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the {\em count-tracking} problem, where there are $k$ players, each holding a counter $n_i$ that gets incremented over time, and the goal is to track an $\eps$-approximation of their sum $n=\sum_i n_i$ continuously at all times, using minimum communication. While the deterministic communication complexity of the problem is $\Theta(k/\eps \cdot \log N)$, where $N$ is the final value of $n$ when the tracking finishes, we show that with randomization, the communication cost can be reduced to $\Theta(\sqrt{k}/\eps \cdot \log N)$. Our algorithm is simple and uses only O(1) space at each player, while the lower bound holds even assuming each player has infinite computing power. Then, we extend our techniques to two related distributed tracking problems: {\em frequency-tracking} and {\em rank-tracking}, and obtain similar improvements over previous deterministic algorithms. Both problems are of central importance in large data monitoring and analysis, and have been extensively studied in the literature.