A Scalable Querying Scheme for Memory-efficient Runtime Models with History

TL;DR

This paper introduces a scalable, memory-efficient querying scheme for runtime models with history, enabling effective history-aware self-adaptation in dynamic systems.

Contribution

It presents a novel querying scheme that integrates temporal requirements with incremental queries, supporting scalable and memory-efficient management of historical runtime models.

Findings

Enables scalable querying of historical runtime models.

Provides memory-efficient storage for runtime models with history.

Facilitates efficient history-aware self-adaptation.

Abstract

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, runtime models support schemes for (runtime) adaptation where data from previous snapshots facilitates more informed decisions. Nevertheless, 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 an implementation.