Towards Highly Scalable Runtime Models with History
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.
Thu 2 Jul Times are displayed in time zone: (UTC) Coordinated Universal Time change
07:00 - 08:20
|Collective Risk Minimization via a Bayesian Model for Statistical Software TestingTechnical|
Joachim HaenselHasso Plattner Institute, University of Potsdam, Germany, Christian Medeiros AdrianoHasso-Plattner-Institute, Potsdam, Johannes DyckHasso Plattner Institute for Software Systems Engineering, Germany, Holger GieseHasso Plattner Institute, University of PotsdamPre-print Media Attached
|Expecting the Unexpected: Developing Autonomous-System Design Principles for Reacting to Unpredicted Events and ConditionsNIER|
Assaf MarronWeizmann Institute of Science, Israel, Lior LimonadIBM Corporation, Israel, Sarah PollackWeizmann Institute of Science, Israel, David HarelWeizmann Institute of Science, IsraelMedia Attached
|Self-Protection Against Business Logic VulnerabilitiesNIER|
Silvan ZellerOmegapoint AB, Sweden, Narges KhakpourLinnaeus University, Danny WeynsKU Leuven, Daniel DeogunOmegapoint AB, SwedenMedia Attached File Attached
|Towards Highly Scalable Runtime Models with HistoryNIER|
Lucas SakizloglouHasso Plattner Institute, University of Potsdam, Sona GhahremaniHasso Plattner Institute, University of Potsdam, Thomas Brand, Matthias BarkowskyHasso Plattner Institute, University of Potsdam, Germany, Holger GieseHasso Plattner Institute, University of PotsdamDOI Pre-print Media Attached
|Q&A and Discussion (Session 5)|