Towards Highly Scalable Runtime Models with History
This addresses the challenge of managing large volumes of monitoring data for IoT systems, enabling more efficient architectural self-adaptation, though it appears incremental as it builds on existing model-driven engineering and rule-based adaptation techniques.
The paper tackles the problem of efficiently storing and retrieving monitoring data for large-scale IoT systems by proposing a highly scalable, history-aware runtime model approach that reduces information accumulation to a required minimum, demonstrating feasibility and scalability in experiments on a simulated smart healthcare system.
Advanced systems such as IoT comprise many heterogeneous, interconnected, and autonomous entities operating in often highly dynamic environments. Due to their large scale and complexity, large volumes of monitoring data are generated and need to be stored, retrieved, and mined in a time- and resource-efficient manner. Architectural self-adaptation automates the control, orchestration, and operation of such systems. This can only be achieved via sophisticated decision-making schemes supported by monitoring data that fully captures the system behavior and its history. Employing model-driven engineering techniques we propose a highly scalable, history-aware approach to store and retrieve monitoring data in form of enriched runtime models. We take advantage of rule-based adaptation where change events in the system trigger adaptation rules. We first present a scheme to incrementally check model queries in the form of temporal logic formulas which represent the conditions of adaptation rules against a runtime model with history. Then we enhance the model to retain only information that is temporally relevant to the queries, therefore reducing the accumulation of information to a required minimum. Finally, we demonstrate the feasibility and scalability of our approach via experiments on a simulated smart healthcare system employing a real-world medical guideline.