A Scalable Querying Scheme for Memory-efficient Runtime Models with History
Runtime models provide a snapshot of a system at runtime at a desired level of abstraction. Via a causal connection to the modeled system and by employing model-driven engineering techniques, models support schemes for runtime adaptation where data from previous snapshots facilitates more informed decisions. Although runtime models and model-based adaptation techniques have been the focus of extensive research, schemes that treat the evolution of the model over time as a first-class citizen have only lately received attention. Consequently, there is a lack of sophisticated technology for such runtime models with history.
We present a querying scheme where the integration of temporal requirements with incremental model queries enables scalable querying for runtime models with history. Moreover, our scheme provides for a memory-efficient storage of such models. By integrating these two features into an adaptation loop, we enable efficient history-aware self-adaptation via runtime models, of which we present a reference implementation.
Fri 23 Oct Times are displayed in time zone: (GMT-04:00) Eastern Time (US & Canada) change
|13:15 - 13:35|
|13:35 - 13:55|
Lucas SakizloglouHasso Plattner Institute, University of Potsdam, Sona GhahremaniHasso Plattner Institute, University of Potsdam, Matthias BarkowskyHasso Plattner Institute, University of Potsdam, Germany, Matthias BarkowskyHasso Plattner Institute, University of Potsdam, Germany, Holger GieseHasso Plattner Institute, University of PotsdamDOI Pre-print
|13:55 - 14:10|
Nicolas Hili, Mojtaba Bagherzadeh, Karim JahedQueen's University, Juergen DingelQueen's University, Kingston, OntarioDOI
|14:10 - 14:25|
Majid BabaeiQueen's University, Mojtaba Bagherzadeh, Juergen DingelQueen's University, Kingston, OntarioPre-print