Statistical Model Checking for Real-Time Database Management Systems: A Case Study
Simin Cai, Barbara Gallina, Dag Nyström, Cristina Seceleanu · 2019
Many industrial control systems manage critical data using Database Management Systems (DBMS). The correctness of transactions, especially their atomicity, isolation and temporal correctness, is essential for the dependability of the entire system. Existing methods and techniques, however, either lack the ability to analyze the interplay of these properties, or do not scale well for systems with large amounts of transactions and data, and complex transaction management mechanisms. In this paper, we propose to analyze large scale real-time database systems using statistical model checking. We propose a pattern-based framework, by extending our previous work, to model the real-time DBMS as a network of stochastic timed automata, which can be analyzed by UPPAAL Statistical Model Checker. We present an industrial case study, in which we design a collision avoidance system for multiple autonomous construction vehicles, via concurrency control of a real-time DBMS. The desired properties of the designed system are analyzed using our proposed framework.