SoftArch/MTE: Generating Distributed System Test-Beds from High-Level Software Architecture Descriptions

Grundy, John; Cai, Yuhong; Liu, Anna

doi:10.1023/b:ause.0000049207.62380.74

Cited by 26 publications

(21 citation statements)

References 27 publications

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“…Our approach is statistical and based on measurement data from the actual target platform. The tool SoftArch/MTE [24] focuses on the evaluation of test-beds generated under various architectures of the application to help choose a particular architecture for the application design. In contrast, we estimate the effect of the migration between toolchains for the same application.…”

Section: Chapter 4 Related Workmentioning

confidence: 99%

Performance prediction upon toolchain migration in model-based software

Ketata

Moreno

Fischmeister

et al. 2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Changing the development environment can have severe impacts on the system behavior such as the execution-time performance. Since it can be costly to migrate a software application, engineers would like to predict the performance parameters of the application under the new environment with as little effort as possible.In this work, we concentrate on model-driven development and provide a methodology to estimate the execution-time performance of application models under different toolchains. Our approach has low cost compared to the migration effort of an entire application. As part of the approach, we provide methods for characterizing model-driven applications, an algorithm for generating application-specific microbenchmarks, and results on using different methods for estimating the performance. In the work, we focus on SCADE as the development toolchain and use a Cruise Control and a Water Level application as case studies to confirm the technical feasibility and viability of our technique.iii Acknowledgements I would like to thank my supervisor Prof. Sebastian Fischmeister for his continuous guidance, insights and support throughout my master. Thank you for giving me the opportunity to work flexibly and for always finding time for me. Your collaboration made the achievement of this work possible.I would also like to thank Carlos Moreno for his fruitful discussions, continuous assistance, and advice. Thank you for being patient and motivating my research.I would like to thank Prof. Krzysztof Czarnecki and Prof. Werner M. Dietl for reading and reviewing my thesis.For the discussions on constraint solvers and model-based applications, and reviewing my work, I would like to thank Jimmy Liang.To my family, thanks for your love and support all these years.To my friends, thanks for making my studies at Waterloo full of joyful moments and great souvenirs.

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Section: Chapter 4 Related Workmentioning

confidence: 99%

Performance prediction upon toolchain migration in model-based software

Ketata

Moreno

Fischmeister

et al. 2015

2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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“…Our project parallels several others using XML and XSLT for code generation. For example, the SoftArch/MTE and Argo/MTE teams have also had favorable experiences using XML + XSLT generators to "glue" off-the-shelf applications together [15], [17]. Likewise, XML+XSLT is advocated for code generation tasks in industry as well [16].…”

Section: Related Workmentioning

confidence: 99%

An automated approach to create, store, and analyze large-scale experimental data in clouds

Jayasinghe

Kimball

Choudhary

et al. 2013

2013 IEEE 14th International Conference on Information Reuse &Amp; Integration (IRI)

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Abstract-The flexibility and scalability of computing clouds make them an attractive application migration target; yet, the cloud remains a black-box for the most part. In particular, their opacity impedes the efficient but necessary testing and tuning prior to moving new applications into the cloud. A natural and presumably unbiased approach to reveal the cloud's complexity is to collect significant performance data by conducting more experimental studies. However, conducting large-scale system experiments is particularly challenging because of the practical difficulties that arise during experimental deployment, configuration, execution and data processing. In this paper we address some of these challenges through Expertus -a flexible automation framework we have developed to create, store and analyze large-scale experimental measurement data. We create performance data by automating the measurement processes for large-scale experimentation, including: the application deployment, configuration, workload execution and data collection processes. We have automated the processing of heterogeneous data as well as the storage of it in a data warehouse, which we have specifically designed for housing measurement data. Finally, we have developed a rich web portal to navigate, statistically analyze and visualize the collected data. Expertus combines template-driven code generation techniques with aspect-oriented programming concepts to generate the necessary resources to fully automate the experiment measurement process. In Expertus, a researcher provides only the high-level description about the experiment, and the framework does everything else. At the end, the researcher can graphically navigate and process the data in the web portal.

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“…Service compositions can be specified in three ways using our toolset: (1) by the use of the Business Process Modelling Notation [21]; (2) by use of our own ViTABaL-WS web service composition notation [14]; and (3) at a more detailed service interface level in MaramaMTE (based on the software architecture notation we developed for our earlier performance engineering tool, ArgoMTE [9]). Using BPMN has the advantage that it is a "standardized" approach to high-level service composition, where services represent business process stages and the notation provides a compositional metaphor.…”

Section: Example Usagementioning

confidence: 99%

“…This simple load test when run will call the findFlight, findSeats and reserveSeat services multiple times with example message input data and delays between each mimicking client application delays. Multiple concurrent instances of this example load test can be run by multi-threading or even running single instances on multiple hosts [9].…”

Section: Example Usagementioning

confidence: 99%

“…Some efforts to performance engineer service-oriented architectures have been attempted, though usually via simulation rather than empirically [7], [8], [13]. Due to the complexity in performance engineering of large distributed systems some tools for this task have been developed [5], [7], [9]. However few of these have been applied to service-oriented software systems.…”

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confidence: 99%

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Performance engineering of service compositions

Grundy

Hosking

et al. 2006

Proceedings of the 2006 International Workshop on Service-Oriented Software Engineering

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While a service-oriented approach to software engineering has become popular in recent times, the actual performance of systems composed from many distributed parts is still largely unpredictable. We describe our recent research applying performance test-bed generation techniques to service-oriented architectural models as an advance on the state of the art in performance engineering of service-oriented software. We outline our related research on tools for business process composition, performance engineering and dynamic system architectures.

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SoftArch/MTE: Generating Distributed System Test-Beds from High-Level Software Architecture Descriptions

Abstract: Abstract

Cited by 26 publications

References 27 publications

Performance prediction upon toolchain migration in model-based software

Performance prediction upon toolchain migration in model-based software

An automated approach to create, store, and analyze large-scale experimental data in clouds

Performance engineering of service compositions

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