inproceedings_hauck.bib
@inproceedings{becker2010a,
abstract = {Legacy applications are still widely spread. If a need to change deployment or update its functionality arises, it becomes difficult to estimate the performance impact of such modifications due to absence of corresponding models. In this paper, we present an extendable integrated environment based on Eclipse developed in the scope of the Q-ImPrESS project for reverse engineering of legacy applications (in C/C++/Java). The Q-ImPrESS project aims at modeling quality attributes at an architectural level and allows for choosing the most suitable variant for implementation of a desired modification. The main contributions of the project include i) a high integration of all steps of the entire process into a single tool, a beta version of which has been already successfully tested on a case study, ii) integration of multiple research approaches to performance modeling, and iii) an extendable underlying meta-model for different quality dimensions.},
author = {Steffen Becker and Michael Hauck and Mircea Trifu and Klaus Krogmann and Jan Kofron},
booktitle = {Proceedings of the 14th European Conference on Software Maintenance and Reengineering, European Projects Track},
keywords = {Q-ImPrESS},
pages = {199-202},
publisher = {IEEE},
title = {{Reverse Engineering Component Models for Quality Predictions}},
url = {http://sdqweb.ipd.kit.edu/publications/pdfs/becker2010a.pdf},
year = {2010}
}
@inproceedings{happe2010b,
abstract = {The broad introduction of multi-core processors made symmetric multiprocessing (SMP) environments mainstream. The additional cores can significantly increase software performance. However, their actual benefit depends on the operating system scheduler's capabilities, the system's workload, and the software's degree of concurrency. The load distribution on the available processors (or cores) strongly influences response times and throughput of software applications. Hence, understanding the operating system scheduler's influence on performance and scalability is essential for the accurate prediction of software performance (response time, throughput, and resource utilisation). Existing prediction approaches tend to approximate the influence of operating system schedulers by abstract policies such as processor sharing and its more sophisticated extensions. However, these abstractions often fail to accurately capture software performance in SMP environments. In this paper, we present a performance Model for general-purpose Operating System Schedulers (MOSS). It allows analyses of software performance taking the influences of schedulers in SMP environments into account. The model is defined in terms of timed Coloured Petri Nets and predicts the effect of different operating system schedulers (e.g., Windows 7, Vista, Server 2003, and Linux 2.6) on software performance. We validated the prediction accuracy of MOSS in a case study using a business information system. In our experiments, the deviation of predictions and measurements was below 10% in most cases and did not exceed 30%.},
acmid = {1906836},
address = {Washington, DC, USA},
author = {Jens Happe and Henning Groenda and Michael Hauck and Ralf H. Reussner},
booktitle = {Proceedings of the 2010 7th International Conference on the Quantitative Evaluation of Systems},
doi = {http://dx.doi.org/10.1109/QEST.2010.15},
isbn = {978-0-7695-4188-4},
numpages = {10},
pages = {59--68},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/happe2010b.pdf},
publisher = {IEEE Computer Society},
series = {QEST '10},
title = {{A Prediction Model for Software Performance in Symmetric Multiprocessing Environments}},
url = {http://dx.doi.org/10.1109/QEST.2010.15},
year = {2010}
}
@inproceedings{hauck2011a,
author = {Michael Hauck and Jens Happe and Ralf Reussner},
booktitle = {Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER 2011)},
http = {http://closer.scitevents.org/},
isbn = {978-989-8425-52-2},
pages = {616--622},
pdf = {http://sdqweb.ipd.uka.de/publications/pdfs/hauck2011a.pdf},
publisher = {SciTePress},
title = {{T}owards {P}erformance {P}rediction for {C}loud {C}omputing {E}nvironments {B}ased on {G}oal-oriented {M}easurements},
year = {2011}
}
@inproceedings{hauck2010a,
abstract = {In symmetric multiprocessing environments, the performance of a software system heavily depends on the application's parallelism, the scheduling and load-balancing policies of the operating system, and the infrastructure it is running on. The scheduling of tasks can influence the response time of an application by several orders of magnitude. Thus, detailed models of the operating system scheduler are essential for accurate performance predictions. However, building such models for schedulers and including them into performance prediction models involves a lot of effort. For this reason, simplified scheduler models are used for the performance evaluation of business information systems in general. In this work, we present an approach to derive load-balancing properties of general-purpose operating system (GPOS) schedulers automatically. Our approach uses goal-oriented measurements to derive performance models based on observations. Furthermore, the derived performance model is plugged into the Palladio Component Model (PCM), a model-based performance prediction approach. We validated the applicability of the approach and its prediction accuracy in a case study on different operating systems.},
author = {Michael Hauck and Jens Happe and Ralf H. Reussner},
booktitle = {Proceedings of the 18th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS'10)},
doi = {10.1109/MASCOTS.2010.44},
isbn = {978-0-7695-4197-6},
issn = {1526-7539},
numpages = {9},
pages = {361--369},
publisher = {IEEE Computer Society},
title = {{Automatic Derivation of Performance Prediction Models for Load-balancing Properties Based on Goal-oriented Measurements}},
url = {http://dx.doi.org/10.1109/MASCOTS.2010.44},
year = {2010}
}
@inproceedings{hauck2011b,
address = {New York, NY, USA},
author = {Michael Hauck and Michael Kuperberg and Nikolaus Huber and Ralf Reussner},
booktitle = {Proceedings of the 7th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2011)},
day = {20--24},
doi = {http://doi.acm.org/10.1145/2000259.2000269},
isbn = {978-1-4503-0724-6},
month = {June},
numpages = {10},
pages = {53--62},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/hauck2011b.pdf},
publisher = {ACM},
title = {{Ginpex: Deriving Performance-relevant Infrastructure Properties Through Goal-oriented Experiments}},
url = {10.1145/2000259.2000269},
year = {2011}
}
@inproceedings{hauck2009b,
abstract = {Software architects often use model-based techniques to analyse performance (e.g. response times), reliability and other extra-functional properties of software systems. These techniques operate on models of software architecture and execution environment, and are applied at design time for early evaluation of design alternatives, especially to avoid implementing systems with insufficient quality. Virtualisation (such as operating system hypervisors or virtual machines) and multiple layers in execution environments (e.g. RAID disk array controllers on top of hard disks) are becoming increasingly popular in reality and need to be reflected in the models of execution environments. However, current component meta-models do not support virtualisation and cannot model individual layers of execution environments. This means that the entire monolithic model must be recreated when different implementations of a layer must be compared to make a design decision, e.g. when comparing different Java Virtual Machines. In this paper, we present an extension of an established model-based performance prediction approach and associated tools which allow to model and predict state-of-the-art layered execution environments, such as disk arrays, virtual machines, and application servers. The evaluation of the presented approach shows its applicability and the resulting accuracy of the performance prediction while respecting the structure of the modelled resource environment.},
author = {Michael Hauck and Michael Kuperberg and Klaus Krogmann and Ralf Reussner},
booktitle = {{Proceedings of the 12th International Symposium on Component Based Software Engineering (CBSE 2009)}},
doi = {10.1007/978-3-642-02414-6_12},
ee = {http://dx.doi.org/10.1007/978-3-642-02414-6_12},
isbn = {978-3-642-02413-9},
number = {5582},
pages = {191--208},
pdf = {http://sdqweb.ipd.uka.de/publications/pdfs/hauck2009b.pdf},
publisher = {Springer},
series = {LNCS},
title = {{Modelling Layered Component Execution Environments for Performance Prediction}},
url = {http://www.comparch-events.org/pages/present.html},
year = {2009}
}
@incollection{HuQuBrHaKo2012-CCaSS-ExpVirtPerfOverhead,
address = {New York},
author = {Huber, Nikolaus and von Quast, Marcel and Brosig, Fabian and Hauck, Michael and Kounev, Samuel},
booktitle = {Cloud Computing and Services Science},
doi = {10.1007/978-1-4614-2326-3_19},
editor = {Ivanov, Ivan and van Sinderen, Marten and Shishkov, Boris},
isbn = {978-1-4614-2325-6},
pages = {353--370},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuQuBrHaKo2012-CCaSS-ExpVirtPerfOverhead.pdf},
publisher = {Springer},
series = {Service Science: Research and Innovations in the Service Economy},
title = {{A Method for Experimental Analysis and Modeling of Virtualization Performance Overhead}},
url = {http://dx.doi.org/10.1007/978-1-4614-2326-3_19},
year = {2012}
}
@inproceedings{HuQuHaKo2011-CLOSER-ModelVirtOverhead,
abstract = {Due to trends like Cloud Computing and Green IT, virtualization technologies are gaining increasing importance. They promise energy and cost savings by sharing physical resources, thus making resource usage more efficient. However, resource sharing and other factors have direct effects on system performance, which are not yet well-understood. Hence, performance prediction and performance management of services deployed in virtualized environments like public and private Clouds is a challenging task. Because of the large variety of virtualization solutions, a generic approach to predict the performance overhead of services running on virtualization platforms is highly desirable. In this paper, we present experimental results on two popular state-of-the-art virtualization platforms, Citrix XenServer 5.5 and VMware ESX 4.0, as representatives of the two major hypervisor architectures. Based on these results, we propose a basic, generic performance prediction model for the two different types of hypervisor architectures. The target is to predict the performance overhead for executing services on virtualized platforms.},
author = {Nikolaus Huber and Marcel von Quast and Michael Hauck and Samuel Kounev},
booktitle = {Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER 2011)},
day = {7--9},
http = {http://closer.scitevents.org/},
isbn = {978-989-8425-52-2},
location = {Noordwijkerhout, The Netherlands},
month = {May},
note = {Acceptance Rate: 18/164 = 10.9\%, Best Paper Award},
pages = {563 -- 573},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuQuHaKo2011-CLOSER-ModelVirtOverhead.pdf},
publisher = {SciTePress},
title = {{E}valuating and {M}odeling {V}irtualization {P}erformance {O}verhead for {C}loud {E}nvironments},
year = {2011}
}
@inproceedings{kramer2012b,
abstract = {Extending metamodels to account for new concerns has a major influence on existing instances, transformations and tools. To minimize the impact on existing artefacts, various techniques for extending a metamodel are available, for example, decorators and annotations. The Palladio Component Model (PCM) is a metamodel for predicting quality of component-based software architectures. It is continuously extended in order to be applicable in originally unexpected domains and settings. Nevertheless, a common extension approach for the PCM and for the tools built on top of it is still missing. In this paper, we propose a lightweight extension approach for the PCM based on profiles and stereotypes to close this gap. Our approach is going to reduce the development effort for new PCM extensions by handling both the definition and use of extensions in a generic way. Due to a strict separation of the PCM, its extension domains, and the connections in between, the approach also increases the interoperability of PCM extensions.},
address = {Karlsruhe},
author = {Max E. Kramer and Zoya Durdik and Michael Hauck and J{\"o}rg Henss and Martin K{\"u}ster and Philipp Merkle and Andreas Rentschler},
booktitle = {Palladio Days 2012 Proceedings (appeared as technical report)},
editor = {Steffen Becker and Jens Happe and Anne Koziolek and Ralf Reussner},
pages = {7--15},
pdf = {http://digbib.ubka.uni-karlsruhe.de/volltexte/documents/2350659},
publisher = {KIT, Faculty of Informatics},
series = {Karlsruhe Reports in Informatics ; 2012,21},
tags = {workshop},
title = {{Extending the Palladio Component Model using Profiles and Stereotypes}},
url = {http://nbn-resolving.org/urn:nbn:de:swb:90-308043},
year = {2012}
}
@inproceedings{westermann2010a,
abstract = {Evaluating the performance (timing behavior, throughput, and resource utilization) of a software system becomes more and more challenging as today's enterprise applications are built on a large basis of existing software (e.g. middleware, legacy applications, and third party services). As the performance of a system is affected by multiple factors on each layer of the system, performance analysts require detailed knowledge about the system under test and have to deal with a huge number of tools for benchmarking, monitoring, and analyzing. In practice, performance analysts try to handle the complexity by focusing on certain aspects, tools, or technologies. However, these isolated solutions are inefficient due to the small reuse and knowledge sharing. The Performance Cockpit presented in this paper is a framework that encapsulates knowledge about performance engineering, the system under test, and analyses in a single application by providing a flexible, plug-in based architecture. We demonstrate the value of the framework by means of two different case studies.},
author = {Dennis Westermann and Jens Happe and Michael Hauck and Christian Heupel},
booktitle = {Proceedings of the 36th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA 2010)},
pages = {31-38},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/westermann2010a.pdf},
publisher = {IEEE Computer Society},
title = {The Performance Cockpit Approach: A Framework for Systematic Performance Evaluations},
year = {2010}
}