A Fault Detection Mechanism for Database Management Systems on Mobile Edge Computing

The ever-increasing demand for reliable data storage solutions at mobile edge computing makes it a necessity to develop timely fault detection mechanisms. In this paper, we address the research challenge of detecting failed data queries in an application and infrastructure agnostic way. In this context, the mobile edge infrastructure includes a Database Management System (DBMS) that runs on server nodes and client nodes that retrieve, store, update and delete data in the DBMS. Specifically, we propose a rule-based algorithm with thresholds that takes as input utilization metrics and detects the faults. It does so without using sensitive DBMS credentials. To verify the applicability and the generality of the proposed algorithm, we made an experimental evaluation with six different query generation functions and testbed configurations. The comparison with other popular machine learning methods used for anomaly detection in monitoring systems, showed that the proposed algorithm significantly surpasses the other methods in terms of Precision and F1-score. During the operation of the mobile edge computing we gathered the utilization metrics, the queries success and fail status and created six datasets that we make them publicly available.