Towards highly scalable runtime models with history

Sakizloglou, Lucas; Ghahremani, Sona; Brand, Thomas; Barkowsky, Matthias; Giese, Holger

doi:10.1145/3387939.3388614

Cited by 9 publications

(9 citation statements)

References 35 publications

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“…Fig. 5 RTM H instances H [5] and H [7] A Runtime Model with History (RTM H ) [95] is an enhanced RTM that simultaneously provides two views on the modeled system: a view of the current system state which corresponds to a conventional causally connected RTM; and a compact view of the history. The view on history is afforded by each entity being equipped with a creation timestamp and a deletion timestamp, abbreviated cts and dts, respectively.…”

Section: Runtime Models and Historymentioning

confidence: 99%

Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains.

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Section: Runtime Models and Historymentioning

confidence: 99%

Incremental execution of temporal graph queries over runtime models with history and its applications

et al. 2021

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“…Logics such as MTGL and MFOTL can be directly applied in the context of runtime monitoring [1,13]. A roadmap towards such an application using an extended subset of MTGL is presented in an informal way in [21,22].…”

Section: Related Workmentioning

confidence: 99%

Optimistic and Pessimistic On-the-fly Analysis for Metric Temporal Graph Logic

Schneider

Sakizloglou

Maximova

et al. 2020

Graph Transformation

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The nonpropositional Metric Temporal Graph Logic (MTGL) specifies the behavior of timed dynamic systems given by timed graph sequences (TGSs), which contain typed attributed graphs representing system states and the elapsed time between states. MTGL satisfaction can be analyzed for finite TGSs by translating its satisfaction problem to the satisfaction problem of nested graph conditions using a folding operation (aggregating a TGS into a graph with history) and a reduction operation (translating an MTGL condition into a nested graph condition). In this paper, we introduce an analysis procedure for MTGL to allow for an on-the-fly analysis of finite/infinite TGSs. To this end, we introduce a further (optimistic) reduction of MTGL conditions, which leads to violations during the on-the-fly analysis only when non-satisfaction is guaranteed in the future whereas the former (pessimistic) reduction leads to violations when satisfaction is not guaranteed in the future. We motivate the relevance of our analysis procedure, which uses both reduction operations, by means of a running example. Finally, we discuss prototypical support in the tool AutoGraph.

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“…However, the history of system changes is not captured and adaptation decisions are only made based on the current system state. The idea of runtime models enriched with history in the form of past event data where queries impose temporal requirements on matched patterns has been, so far, treated only preliminarily and ad hoc, e.g., by a manual translation of a single example of a restricted form that supports only past requirements [49]. In this paper, we extend the scheme envisioned in [49] and present a version which lifts the restrictions, supports complex queries with both past and future requirements, and their systematic operationalization.…”

Section: Introductionmentioning

confidence: 99%

“…The idea of runtime models enriched with history in the form of past event data where queries impose temporal requirements on matched patterns has been, so far, treated only preliminarily and ad hoc, e.g., by a manual translation of a single example of a restricted form that supports only past requirements [49]. In this paper, we extend the scheme envisioned in [49] and present a version which lifts the restrictions, supports complex queries with both past and future requirements, and their systematic operationalization. We extend the scheme further to support a full online adaptation cycle employing incremental model queries, which enables history-aware adaptations where real-time, efficient storage and querying for data generated by events are key.…”

Section: Introductionmentioning

confidence: 99%

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

Sakizloglou,

Ghahremani,

Barkowsky

et al. 2020

Preprint

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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 facilitate 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.

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Towards highly scalable runtime models with history

Cited by 9 publications

References 35 publications

Incremental execution of temporal graph queries over runtime models with history and its applications

Incremental execution of temporal graph queries over runtime models with history and its applications

Optimistic and Pessimistic On-the-fly Analysis for Metric Temporal Graph Logic

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

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