ScaleCheck: A Single-Machine Approach for Discovering Scalability Bugs in Large Distributed Systems


Cesar A. Stuardo, University of Chicago; Tanakorn Leesatapornwongsa, Samsung Research America; Riza O. Suminto, Huan Ke, and Jeffrey F. Lukman, University of Chicago; Wei-Chiu Chuang, Cloudera; Shan Lu and Haryadi S. Gunawi, University of Chicago


We present ScaleCheck, an approach for discovering scalability bugs (a new class of bug in large storage systems) and for democratizing large-scale testing. ScaleCheck employs a program analysis technique, for finding potential causes of scalability bugs, and a series of colocation techniques, for testing implementation code at real scales but doing so on just a commodity PC. ScaleCheck has been integrated to several large-scale storage systems, Cassandra, HDFS, Riak, and Voldemort, and successfully exposed known and unknown scalability bugs, up to 512-node scale on a 16-core PC.

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@inproceedings {227814,
author = {Cesar A. Stuardo and Tanakorn Leesatapornwongsa and Riza O. Suminto and Huan Ke and Jeffrey F. Lukman and Wei-Chiu Chuang and Shan Lu and Haryadi S. Gunawi},
title = {ScaleCheck: A Single-Machine Approach for Discovering Scalability Bugs in Large Distributed Systems},
booktitle = {17th {USENIX} Conference on File and Storage Technologies ({FAST} 19)},
year = {2019},
isbn = {978-1-931971-48-5},
address = {Boston, MA},
pages = {359--373},
url = {},
publisher = {{USENIX} Association},