@inproceedings{9c76b50df59945c18c953c5715464614,
title = "CELL: Counter estimation for per-flow traffic over sliding windows",
abstract = "Estimators reduce the memory footprint of maintaining network statistics, while keeping the estimation error of each flow proportional to its size. This is unlike sketches and other approximate algorithms that only guarantee an error proportional to the entire stream size. In this work we present the CELL algorithm that combines estimators with efficient flow representation to obtain superior memory reduction compared to the state of the art. We also extend CELL to the sliding window model, which priorities recent data over old one, by presenting two variants named RAND-CELL and SHIFT-CELL.",
author = "Rana Shahout and Dolev Adas and Roy Friedman",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 14th ACM International Conference on Systems and Storage, SYSTOR 2021 ; Conference date: 14-06-2021 Through 16-06-2021",
year = "2021",
month = jun,
day = "14",
doi = "10.1145/3456727.3463826",
language = "الإنجليزيّة",
series = "SYSTOR 2021 - Proceedings of the 14th ACM International Conference on Systems and Storage",
booktitle = "SYSTOR 2021 - Proceedings of the 14th ACM International Conference on Systems and Storage",
}