Randomized Admission Policy for Efficient Top-k and Frequency Estimation
Abstract
Network management protocols often require timely and meaningful insight about per flow network traffic. This paper introduces Randomized Admission Policy (RAP) - a novel algorithm for the frequency and top-k estimation problems, which are fundamental in network monitoring. We demonstrate space reductions compared to the alternatives by a factor of up to 32 on real packet traces and up to 128 on heavy-tailed workloads. For top-k identification, RAP exhibits memory savings by a factor of between 4 and 64 depending on the skew of the workload. These empirical results are backed by formal analysis, indicating the asymptotic space improvement of our probabilistic admission approach. Additionally, we present d-Way RAP, a hardware friendly variant of RAP that empirically maintains its space and accuracy benefits.
Keywords
Cite
@article{arxiv.1612.02962,
title = {Randomized Admission Policy for Efficient Top-k and Frequency Estimation},
author = {Ran Ben Basat and Gil Einziger and Roy Friedman and Yaron Kassner},
journal= {arXiv preprint arXiv:1612.02962},
year = {2016}
}
Comments
Conference version accepted to IEEE INFOCOM2017