Revel8or: Model Driven Capacity Planning Tool Suite

Zhu, Liming; Liu, Yan; Bui, Ngoc Bao; Gorton, Ian

doi:10.1109/icse.2007.73

Cited by 19 publications

(9 citation statements)

References 5 publications

(7 reference statements)

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“…A company pilot of a DSL is described in [24], but our goal is to evaluate a DSL with professionals before it gains sufficient maturity to be piloted. A solution to the problem of evaluating the performance of DSL models on various targets is described in [22]. This is an example of DSL evaluation that does not involve professionals.…”

Section: A Choice Of Methodsmentioning

confidence: 99%

Assessing the industrial applicability and adoption potential of the AUKOTON model driven control application engineering approach

Vepsäläinen

Sierla

Peltola

et al. 2010

2010 8th IEEE International Conference on Industrial Informatics

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Challenges in software development for automation and control have increased because of various reasons, including the size of applications and competition within the industry. A potential solution could be the use of a model-driven engineering approach to facilitate the development process and design information flow. Despite the wide interests towards modelbased techniques in the automation domain, the authors have not been able to find a complete, tool supported UML based MDE approach, the industrial relevance of which would have been systematically assessed. In this paper, we intend to assess the suitability and usefulness of the AUKOTON development process and tools to development of industrial automation and control applications. To study these questions, we organized a laboratory project, in which industrial professionals used our development process and tools, and used qualitative research methods for gathering the industrial feedback.

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Section: A Choice Of Methodsmentioning

confidence: 99%

Assessing the industrial applicability and adoption potential of the AUKOTON model driven control application engineering approach

Vepsäläinen

Sierla

Peltola

et al. 2010

2010 8th IEEE International Conference on Industrial Informatics

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“…In addition, the approach lacks tool support. Zhu et al [38,39] extend the approach by a model-driven benchmark generation tool. The authors focus on benchmarks that are generated based on a software architecture description, whereas our approach employs model-driven load generation of infrastructure experiments agnostic to the software system under prediction.…”

Section: Related Workmentioning

confidence: 99%

Deriving performance-relevant infrastructure properties through model-based experiments with Ginpex

Hauck

Kuperberg²,

Huber

et al. 2013

Softw Syst Model

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To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Goal-oriented INfrastructure Performance EXperiments (Ginpex) approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties.

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“…A model-driven capacity planning tool suite for component-and web service-based applications is proposed by Zhu et al [25]. The tool suite can be used in early software design phases to support the performance evaluation.…”

Section: Capacity Planning Using Performance Modelsmentioning

confidence: 99%

Using architecture-level performance models as resource profiles for enterprise applications

Brunnert

Wischer

Krcmar

2014

Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures

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The rising energy and hardware demand is a growing concern in enterprise data centers. It is therefore desirable to limit the hardware resources that need to be added for new enterprise applications (EA). Detailed capacity planning is required to achieve this goal. Otherwise, performance requirements (i.e. response time, throughput, resource utilization) might not be met. This paper introduces resource profiles to support capacity planning. These profiles can be created by EA vendors and allow evaluating energy consumption and performance of EAs for different workloads and hardware environments. Resource profiles are based on architecture-level performance models. These models allow to represent performance-relevant aspects of an EA architecture separately from the hardware environment and workload. The target hardware environment and the expected workload can only be specified by EA hosts and users respectively. To account for these distinct responsibilities, an approach is introduced to adapt resource profiles created by EA vendors to different hardware environments. A case study validates this concept by creating a resource profile for the SPECjEnterprise2010 benchmark application. Predictions using this profile for two hardware environments match energy consumption and performance measurements with an error of mostly below 15 %.

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Revel8or: Model Driven Capacity Planning Tool Suite

Cited by 19 publications

References 5 publications

Assessing the industrial applicability and adoption potential of the AUKOTON model driven control application engineering approach

Assessing the industrial applicability and adoption potential of the AUKOTON model driven control application engineering approach

Deriving performance-relevant infrastructure properties through model-based experiments with Ginpex

Using architecture-level performance models as resource profiles for enterprise applications

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