Runtime monitoring plays a key role in the assurance of modern intelligent cyber-physical systems, which are frequently data-intensive and safety-critical. While graph queries can serve as an expressive yet formally precise specification language to capture the safety properties of interest, there are no timeliness guarantees for such auto-generated runtime monitoring programs, which prevents their use in a real-time setting. While worst-case execution time (WCET) bounds derived by existing static WCET estimation techniques are safe, they may not be tight as they are unable to exploit domain-specific (semantic) information about the input models. This article presents a semantic-aware WCET analysis method for data-driven monitoring programs derived from graph queries. The method incorporates results obtained from low-level timing analysis into the objective function of a modern graph solver. This allows the systematic generation of input graph models up to a specified size (referred to as witness models ) for which the monitor is expected to take the most time to complete. Hence, the estimated execution time of the monitors on these graphs can be considered as safe and tight WCET. Additionally, we perform a set of experiments with query-based programs running on a real-time platform over a set of generated models to investigate the relationship between execution times and their estimates, and we compare WCET estimates produced by our approach with results from two well-known timing analyzers, aiT and OTAWA.

Abstract Smart cyber-physical systems (CPSs) have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous and distributed computation platform with resource-constrained devices to monitor, manage and control autonomous behavior. First, we propose a distributed runtime model to capture the operational state and the context information of a smart CPS using directed, typed and attributed graphs as high-level knowledge representation. The runtime model is distributed among the participating nodes, and it is consistently kept up to date in a continuously evolving environment by a time-triggered model management protocol. Our runtime models offer a (domain-specific) model query and manipulation interface over the reliable communication middleware of the Data Distribution Service (DDS) standard widely used in the CPS domain. Then, we propose to carry out distributed runtime monitoring by capturing critical properties of interest in the form of graph queries, and design a distributed graph query evaluation algorithm for evaluating such graph queries over the distributed runtime model. As the key innovation, our (1) distributed runtime model extends existing publish–subscribe middleware (like DDS) used in real-time CPS applications by enabling the dynamic creation and deletion of graph nodes (without compile time limits). Moreover, (2) our distributed query evaluation extends existing graph query techniques by enabling query evaluation in a real-time, resource-constrained environment while still providing scalable performance. Our approach is illustrated, and an initial scalability evaluation is carried out on the MoDeS3 CPS demonstrator and the open Train Benchmark for graph queries.

Recent approaches in runtime monitoring and live data analytics have started to use expressive graph queries at runtime to capture and observe properties of interest at a high level of abstraction. However, in a critical context, such applications often require timeliness guarantees, which have not been investigated yet for query-based solutions due to limitations of existing static worst-case execution time (WCET) analysis techniques. One limitation is the lack of support for dynamic memory allocation, which is required by the dynamically evolving runtime models on which the queries are evaluated. Another open challenge is to compute WCET for asynchronously communicating programs such as distributed monitors. This paper introduces our vision about how to assess such timeliness properties and how to provide tight WCET estimates for query execution at runtime over a dynamic model. Furthermore, we present an initial solution that combines state-of-the-art parametric WCET estimations with model statistics and search plans of queries.

In safety-critical cyber-physical systems (CPS), a service failure may result in severe financial loss or damage in human life. Smart CPSs have complex interaction with their environment which is rarely known in advance, and they heavily depend on intelligent data processing carried out over a heterogeneous computation platform and provide autonomous behavior. This complexity makes design time verification infeasible in practice, and many CPSs need advanced runtime monitoring techniques to ensure safe operation. While graph queries are a powerful technique used in many industrial design tools of CPSs, in this paper, we propose to use them to specify safety properties for runtime monitors on a high-level of abstraction. Distributed runtime monitoring is carried out by evaluating graph queries over a distributed runtime model of the system which incorporates domain concepts and platform information. We provide a semantic treatment of distributed graph queries using 3-valued logic. Our approach is illustrated and an initial evaluation is carried out using the MoDeS3 educational demonstrator of CPSs.

Model-based systems engineering of critical cyber-physical systems necessitates effective collaboration between different stakeholders while still providing secure protection of intellectual properties of all involved parties. While engineering artifacts are frequently stored in version control repositories, secure access control is limited to file-level strategies in most existing frameworks where models are split into multiple fragments with all-or-nothing permissions, which becomes a scalability and usability bottleneck in case of complex industrial models.