Towards Close-to-Zero Runtime Collection Overhead: Raft-Based Anomaly Diagnosis on System Faults for Distributed Storage System

Distributed storage systems are fundamental infrastructures of today’s large-scale software systems such as cloud systems. Diagnosing anomalies in distributed storage systems is essential for maintaining software availability. Existing anomaly diagnosis approaches mainly rely on the run-time data including monitoring data and application logs. However, collecting and analyzing the run-time data requires huge computing, storage, and management costs. Typically, more fine-grained run-time data can reveal more symptoms of anomalies, but on the contrary, requires more computing, storage, and management costs. As a result, solving the anomaly diagnosis problem is a balancing between the quality of run-time data and system overhead or cost. In this paper, we take into account both data quality and system overhead or cost by introducing a new type of run-time data-Raft logs. Raft logs are naturally produced by distributed storage systems and collecting raft logs will not bring any extra system overhead. To verify the ability of Raft logs in reflecting anomalies, we conduct a comprehensive study on the interconnection between the anomalies and Raft logs. Based on the study, we propose an effective <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b>aft-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b>ased <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>nomaly <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b>iagnosis approach named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RBAD</b>. For evaluation, we expose the first open-sourced comprehensive dataset with multiple runtime data containing both Raft logs, application logs and monitoring data. Experiments based on this dataset demonstrate RBAD’s superiority, outperforming monitoring-based methods by 15.38% and log-based methods by 53.10%.