Understanding and Detecting Scalability Faults in Large-Scale Distributed Systems
For developers and operators of large-scale distributed systems, this work provides a systematic understanding and automated detection of scalability faults that are otherwise hard to uncover.
This paper presents the first comprehensive study on scalability faults in large-scale distributed systems, analyzing 444 issue reports from 10 systems. The authors propose ScaleLens, a detection approach that combines dynamic and static analyses, which detects 4.2x more dimensional code fragments associated with known faults than the baseline and identifies 334 problematic fragments in Cassandra, HDFS, and Ignite.
Scalable distributed systems form the backbone of modern computing infrastructure. However, as scale grows, system complexity may lead to scalability faults. Scalability faults are challenging to uncover and diagnose, as they are often latent and only manifest at large-scale deployment. In this paper, we present the first comprehensive study on scalability faults and propose an approach for their detection. First, we systematically investigate 444 scalability issue reports from 10 large-scale distributed systems to understand the common anti-patterns and root causes of scalability faults. We found that the majority of these faults are caused by the synergy between dimensional code fragments and anti-patterns associated with them. Second, based on our findings, we design and implement ScaleLens, a novel approach to detect scalability faults. ScaleLens combines dynamic and static analyses to pinpoint dimensional code fragments and match them with anti-patterns. Our evaluation shows that ScaleLens detects 4.2x more dimensional code fragments associated with known scalability faults compared to the baseline. On the latest stable versions of Cassandra, HDFS, and Ignite, ScaleLens detects 334 dimensional code fragments with confirmed problematic behavior.