DIADS: Addressing the “my-problem-or-yours” syndrome with integrated SAN and database diagnosis

Abstract

We present DIADS, an integrated DIAgnosis tool for Databases and Storage area networks (SANs). Existing diagnosis tools in this domain have a database-only (e.g., [11]) or SAN-only (e.g., [28]) focus. DIADS is a first-of-a-kind framework based on a careful integration of information from the database and SAN subsystems; and is not a simple concatenation of database-only and SAN-only modules. This approach not only increases the accuracy of diagnosis, but also leads to significant improvements in efficiency. DIADS uses a novel combination of non-intrusive machine learning techniques (e.g., Kernel Density Estimation) and domain knowledge encoded in a new symptoms database design. The machine learning component provides core techniques for problem diagnosis from monitoring data, and domain knowledge acts as checks-and-balances to guide the diagnosis in the right direction. This unique system design enables DIADS to function effectively even in the presence of multiple concurrent problems as well as noisy data prevalent in production environments. We demonstrate the efficacy of our approach through a detailed experimental evaluation of DIADS implemented on a real data center testbed with PostgreSQL databases and an enterprise SAN.