On Competitive Algorithms for Approximations of Top-k-Position Monitoring of Distributed Streams

TL;DR

This paper introduces new algorithms for approximate top-k position monitoring in distributed streams, reducing communication by tolerating small errors, and analyzes their competitiveness against different adversaries.

Contribution

It extends previous work by developing and analyzing new filter-based algorithms for approximate top-k monitoring with improved competitiveness analysis.

Findings

New algorithms for approximate top-k monitoring are proposed.

Analysis shows significant differences in adversary power between exact and approximate filters.

Communication costs are reduced by allowing approximation in monitoring.

Abstract

Consider the continuous distributed monitoring model in which $n$ distributed nodes, receiving individual data streams, are connected to a designated server. The server is asked to continuously monitor a function defined over the values observed across all streams while minimizing the communication. We study a variant in which the server is equipped with a broadcast channel and is supposed to keep track of an approximation of the set of nodes currently observing the $k$ largest values. Such an approximate set is exact except for some imprecision in an $\varepsilon$-neighborhood of the $k$-th largest value. This approximation of the Top-$k$-Position Monitoring Problem is of interest in cases where marginal changes (e.g. due to noise) in observed values can be ignored so that monitoring an approximation is sufficient and can reduce communication. This paper extends our results from [IPDPS'15], where we have developed a filter-based online algorithm for the (exact) Top-k-Position Monitoring Problem. There we have presented a competitive analysis of our algorithm against an offline adversary that also is restricted to filter-based algorithms. Our new algorithms as well as their analyses use new methods. We analyze their competitiveness against adversaries that use both exact and approximate filter-based algorithms, and observe severe differences between the respective powers of these adversaries.