An Anomaly Detection Approach for Scale-Out Storage Systems

Scale-out storage systems (SoSS) have become increasingly important for meeting availability requirements of web services in cloud platforms. To enhance data availability, SoSS rely on a variety of built-in fault-tolerant mechanisms, including replication, redundant network topologies, advanced request scheduling, and other failover techniques. However, performance issues in cloud services still remain one of the main causes of discontentment among their tenants. In this paper, we propose an anomaly detection approach for SoSS that predicts cloud anomalies caused by memory and network faults. To evaluate our prediction model, we built a testbed simulating a virtual data center using VMware. Experimental results confirm that the injected faults are likely to undermine the data availability in SoSS. They suggest that although unsupervised learning has been the most common method for anomaly detection, a supervisedbased implementation of the same model reduces the false positive rate by roughly 10%. Our analysis also points out that probing SoSS-specific monitoring data at the VM-level contributes to improve the anomaly prediction efficiency.