Towards automated provenance collection for runtime models to record system history

Abstract

In highly dynamic environments, systems are expected to make decisions on the fly based on their observations that are bound to be partial. As such, the reasons for its runtime behaviour may be difficult to understand. In these cases, accountability is crucial, and decisions by the system need to be traceable. Logging is essential to support explanations of behaviour, but it poses challenges. Concerns about analysing massive logs have motivated the introduction of structured logging, however, knowing what to log and which details to include is still a challenge. Structured logs still do not necessarily relate events to each other, or indicate time intervals. We argue that logging changes to a runtime model in a provenance graph can mitigate some of these problems. The runtime model keeps only relevant details, therefore reducing the volume of the logs, while the provenance graph records causal connections between the changes and the activities performed by the agents in the system that have introduced them. In this paper, we demonstrate a first version towards a reusable infrastructure for the automated construction of such a provenance graph. We apply it to a multithreaded traffic simulation case study, with multiple concurrent agents managing different parts of the simulation. We show how the provenance graphs can support validating the system behaviour, and how a seeded fault is reflected in the provenance graphs. CCS CONCEPTS • Software and its engineering → System modeling languages; Integration frameworks; Model-driven software engineering.

Cronista: A multi-database automated provenance collection system for runtime-models

“…In addition to providing updated and expanded descriptions of the observer and curator components, this paper has expanded the evaluation by comparing the trade-offs involved in storing the provenance graph in a model repository (CDO) and graph database (JanusGraph over the vendor-agnostic TinkerPop API). This comparison has been enabled by the improved architecture since our prior work [42]. The JanusGraph-based history model store introduced a more user-friendly query language with Gremlin.…”

AMon: A domain-specific language and framework for adaptive monitoring of Cyber–Physical Systems

“…Its focus lies on supporting runtime monitoring activities for real-time and embedded systems and it does not adapt monitors based on different states of the system. A related approach for tracking the behavior of self-adaptive systems (Reynolds et al, 2020) relies on provenance graphs and a runtime model to analyze and explain the runtime behavior of a system. In the domain of robot systems, a model-based framework (Corbato et al, 2020) adapts robot control architectures at runtime.…”

Computing performance requirements for web service compositions

“…Recently, we have considered provenance graphs to log changes to a run-time model [29]. However, the notation is far from ours and the focus is on showing how provenance graphs can support the validation of systems.…”