Generating Synchronization Engines between Running Systems and Their Model-Based Views

Song, Hui; Xiong, Yingfei; Chauvel, Franck; Huang, Gang; Hu, Zhenjiang; Mei, Hong

doi:10.1007/978-3-642-12261-3_14

Cited by 33 publications

(35 citation statements)

References 17 publications

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“…Based on our earlier work [5,6], we provide a reflective runtime model for SmartLab, and an excerpt of its meta-model is shown in the left of Figure 1. The classes directly define the concepts specific to the devices, and the properties define the data that can be retrieved from them.…”

Section: The Runtime Models For Smartlabmentioning

confidence: 99%

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Instant and Incremental QVT Transformation for Runtime Models

Song

Huang

Chauvel

et al. 2011

Model Driven Engineering Languages and Systems

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Abstract. As a dynamic representation of the running system, a runtime model provides a model-based interface to monitor and control the system. A key issue for runtime models is to maintain their causal connections with the running system. That means when the systems change, the models should change accordingly, and vice versa. However, for the abstract runtime models that are heterogeneous to their target systems, it is challenging to maintain such causal connections. This paper presents a model-transformation-based approach to maintaining causal connections for abstract runtime models. We define a new instant and incremental transformation semantics for the QVT-Relational language, according to the requirements of runtime models, and develop the transformation algorithm following this semantics. We implement this approach on the mediniQVT transformation engine, and apply it to provide the runtime model for an intelligent office system named SmartLab.

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Section: The Runtime Models For Smartlabmentioning

confidence: 99%

“…Many research approaches provide structural runtime models for different systems [2][3][4][5]. These approaches focus on wrapping the low-level management capability of the target systems into model-based interfaces, and thus their runtime models directly reflect the system data.…”

Section: Introductionmentioning

confidence: 99%

Instant and Incremental QVT Transformation for Runtime Models

Song

Huang

Chauvel

et al. 2011

Model Driven Engineering Languages and Systems

Self Cite

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“…Utilizing the meta-modeling tool presented in this paper, we upgrade an original runtime model case on JOnAS [4] with a more complete meta-model, which is automatically inferred from two API clients: 1) an official sample shipped with the release version of JOnAS, and 2) the official web-based JOnAS administration console, jonasAdmin, which organizes and displays the runtime data by HTML pages. The clients and the meta-models are described in Table 1.…”

Section: The Jonas Case Studymentioning

confidence: 99%

“…The MoDisco project [6] provides reusable components for "model discovers" that represent system data as MOF compliant models, according to the MOF meta-models. The authors of this paper also present a SM@RT tool [4] to generate a runtime model synchronizer from a MOF meta-model. These tools all require users to provide a meta-model first.…”

Section: Related Workmentioning

confidence: 99%

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Inferring Meta-models for Runtime System Data from the Clients of Management APIs

Song¹,

Huang²,

Xiong³

et al. 2010

Model Driven Engineering Languages and Systems

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Abstract.A new trend in runtime system monitoring is to utilize MOFbased techniques in analyzing the runtime system data. Approaches and tools have been proposed to automatically reflect the system data as MOF compliant models, but they all require users to manually build the meta-models that define the types and relations of the system data. To do this, users have to understand the different management APIs provided by different systems, and find out what kinds of data can be obtained from them. In this paper, we present an automated approach to inferring such meta-models by analyzing client code that accesses management APIs. A set of experiments show that the approach is useful for realizing runtime models and applicable to a wide range of systems, and the inferred meta-models are close to the reference ones.

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Mechanisms for Leveraging Models at Runtime in Self-adaptive Software

et al. 2014

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Abstract. Modern software systems are often required to adapt their behavior at runtime in order to maintain or enhance their utility in dynamic environments. Models at runtime research aims to provide suitable abstractions, techniques, and tools to manage the complexity of adapting software systems at runtime. In this chapter, we discuss challenges associated with developing mechanisms that leverage models at runtime to support runtime software adaptation. Specifically, we discuss challenges associated with developing effective mechanisms for supervising running systems, reasoning about and planning adaptations, maintaining consistency among multiple runtime models, and maintaining fidelity of runtime models with respect to the running system and its environment. We discuss related problems and state-of-the-art mechanisms, and identify open research challenges.

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Generating Synchronization Engines between Running Systems and Their Model-Based Views

Cited by 33 publications

References 17 publications

Instant and Incremental QVT Transformation for Runtime Models

Instant and Incremental QVT Transformation for Runtime Models

Inferring Meta-models for Runtime System Data from the Clients of Management APIs

Mechanisms for Leveraging Models at Runtime in Self-adaptive Software

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