inproceedings_stier.bib
@inproceedings{Khachatryan2012,
acmid = {2352474},
address = {Berlin, Heidelberg},
author = {Khachatryan, Andranik and M\"{u}ller, Emmanuel and Stier, Christian and B\"{o}hm, Klemens},
booktitle = {Proceedings of the 24th International Conference on Scientific and Statistical Database Management},
doi = {10.1007/978-3-642-31235-9_22},
isbn = {978-3-642-31234-2},
location = {Chania, Crete, Greece},
numpages = {9},
pages = {334--342},
publisher = {Springer-Verlag},
series = {SSDBM},
title = {{Sensitivity of Self-tuning Histograms: Query Order Affecting Accuracy and Robustness}},
url = {http://dx.doi.org/10.1007/978-3-642-31235-9_22},
year = {2012}
}
@inproceedings{merkle2014a,
address = {New York, NY, USA},
author = {Philipp Merkle and Christian Stier},
booktitle = {Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering},
location = {Dublin, Ireland},
note = {Work-In-Progress Paper},
publisher = {ACM},
series = {ICPE '14},
title = {{Modelling Database Lock-Contention in Architecture-level Performance Simulation}},
year = {2014}
}
@inproceedings{ostberg2014a,
address = {Singapore},
author = {P-O \"{O}stberg and Henning Groenda and Stefan Wesner and James Byrne and Dimitrios~S. Nikolopoulos and Craig Sheridan and Jakub Krzywda and Ahmed Ali-Eldin and Johan Tordsson and Erik Elmroth and Christian Stier and Klaus Krogmann and J\"{o}rg Domaschka and Christopher Hauser and PJ Byrne and Sergej Svorobej and Barry McCollum and Zafeirios Papazachos and Loke Johannessen and Stephan R\"{u}th and Dragana Paurevic},
booktitle = {Proceedings of the Sixth IEEE International Conference on Cloud Computing Technology and Science (CloudCom)},
doi = {10.1109/CloudCom.2014.62},
month = {December},
pages = {26-31},
publisher = {IEEE Computer Society},
title = {{The CACTOS Vision of Context-Aware Cloud Topology Optimization and Simulation}},
year = {2014}
}
@inproceedings{stier2015a,
author = {Stier, Christian and Koziolek, Anne and Groenda, Henning and Reussner, Ralf},
booktitle = {Proceedings of the 9th European Conference on Software Architecture (ECSA '15)},
location = {Dubrovnik/Cavtat, Croatia},
note = {Acceptance Rate (Full Paper): 15/80 = 18.8\%},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/stier2015a.pdf},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {{Model-Based Energy Efficiency Analysis of Software Architectures}},
year = {2015},
doi = {10.1007/978-3-319-23727-5_18},
url = {http://dx.doi.org/10.1007/978-3-319-23727-5_18},
abstract = {Design-time quality analysis of software architectures evaluates the impact of design decisions in quality dimensions such as performance. Architectural design decisions decisively impact the energy efficiency (EE) of software systems. Low EE not only results in higher operational cost due to power consumption. It indirectly necessitates additional capacity in the power distribution infrastructure of the target deployment environment. Methodologies that analyze EE of software systems are yet to reach an abstraction suited for architecture-level reasoning. This paper outlines a model-based approach for evaluating the EE of software architectures. First, we present a model that describes the central power consumption characteristics of a software system. We couple the model with an existing model-based performance prediction approach to evaluate the consumption characteristics of a software architecture in varying usage contexts. Several experiments show the accuracy of our architecture-level consumption predictions. Energy consumption predictions reach an error of less than 5.5% for stable and 3.7% for varying workloads. Finally, we present a round-trip design scenario that illustrates how the explicit consideration of EE supports software architects in making informed trade-off decisions between performance and EE.}
}
@inproceedings{Svorobej2015,
author = {Sergej Svorobej and James Byrne and Paul Liston and PJ Byrne and Christian Stier and Henning Groenda and Zafeirios Papazachos and Dimitrios Nikolopoulos},
booktitle = {Eighth EAI International Conference on Simulation Tools and Techniques (SIMUTOOLS)},
doi = {10.4108/eai.24-8-2015.2261129},
keywords = {modelling, cloud computing, simulation integration, data collection},
month = {August},
publisher = {ACM},
title = {Towards Automated Data-Driven Model Creation for Cloud Computing Simulation},
year = {2015}
}
@inproceedings{groenda2015c,
author = {Groenda, Henning and Stier, Christian},
booktitle = {Symposium on Software Performance 2015},
title = {{Improving IaaS Cloud Analyses by Black-Box Resource Demand Modeling}},
year = {2015},
url = {https://sdqweb.ipd.kit.edu/publications/pdfs/groenda2015c.pdf}
}
@inproceedings{stier2016a,
author = {Stier, Christian and Koziolek, Anne},
booktitle = {2016 12th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA)},
location = {Venice, Italy},
publisher = {ACM},
series = {QoSA'16},
tags = {refereed},
title = {{Considering Transient Effects of Self-Adaptations in Model-Driven Performance Analyses}},
year = {2016},
doi = {10.1109/QoSA.2016.14},
pdf = {https://sdqweb.ipd.kit.edu/publications/pdfs/stier2016a.pdf},
abstract = {Model-driven performance engineering allows software architects to reason on performance characteristics of a software system in early design phases. In recent years, model-driven analysis techniques have been developed to evaluate performance characteristics of self-adaptive software systems. These techniques aim to reason on the ability of a self-adaptive software system to fulfill performance requirements in transient phases. A transient phase is the interval in which the behavior of the system changes, e.g., due to a burst in user requests. However, the effectiveness and efficiency with which a system is able to adapt depends not only on the time when it triggers adaptation actions but also on the time at which they are completed. Executing an adaptation action can cause additional stress on the adapted system. This can further impede the performance of the system in the transient phase. Model-driven analyses of self-adaptive software do not consider these transient effects. This paper outlines an approach for evaluating transient effects in model-driven analyses of self-adaptive software systems. The evaluation applied our approach to a horizontally scaling media hosting application in three experiments. By considering the delay in booting new Virtual Machines (VMs), we were able to improve the accuracy of predicted response times. The second and third experiment demonstrated that the increased accuracy enables an early detection and resolution of design deficiencies of self-adaptive software systems.}
}
@inproceedings{stier2016b,
author = {Stier, Christian and Groenda, Henning},
booktitle = {Proceedings of the Modeling and Simulation of Complexity in Intelligent, Adaptive and Autonomous Systems 2016 (MSCIAAS 2016) and Space Simulation for Planetary Space Exploration (SPACE 2016)},
series = {MSCIAAS},
isbn = {978-1-5108-2319-8},
location = {Pasadena, CA, USA},
publisher = {Society for Computer Simulation International},
series = {MSCIAAS},
pages = {2:1--2:8},
articleno = {2},
numpages = {8},
url = {http://dl.acm.org/citation.cfm?id=2962664.2962666},
title = {{Ensuring Model Continuity when Simulating Self-Adaptive Software Systems}},
year = {2016},
abstract = {Self-adaptivity in software systems aims to balance the use of costly resources, i.e. of servers and energy, under given constraints such as Quality of Service (QoS) requirements. Simulation does not require risky testing in running systems and has less assumptions and limitations than formal verification when evaluating the effect of self-adaptation mechanisms. Existing simulation frameworks for analyzing self-adaptive software systems require re-implementing algorithms to conform to the abstraction and interfaces of the simulation framework. We present an approach for coupling simulation-based analyses of self-adaptive software systems with self-adaptation mechanisms that eliminates the need to re-implement the mechanisms and ensures model continuity. The evaluation demonstrates the low complexity required when our approach is used to ensure model continuity between simulation and self-adaptation framework. It presents the results of two experiments we performed after coupling the SimuLizar simulation framework and the CACTOS runtime management framework for Cloud platforms. With this coupling, Cloud data center operators benefit from what-if-analyses of self-adaptation mechanisms and software engineers can optimize the QoS of systems on the drawing board without acquiring deep knowledge of simulation internals.}
}
@incollection{lehrig2016a,
author = {Sebastian Lehrig and Steffen Becker and Christian Stier and Ralf H. Reussner},
title = {Future Trends},
pages = {339--342},
chapter = {15},
booktitle = {Modeling and Simulating Software Architectures -- The Palladio Approach},
publisher = {MIT Press},
year = {2016},
editor = {Reussner, Ralf H. and Becker, Steffen and Happe, Jens and Heinrich, Robert and Koziolek, Anne and Koziolek, Heiko and Kramer, Max and Krogmann, Klaus},
address = {Cambridge, MA},
month = {October},
url = {http://mitpress.mit.edu/books/modeling-and-simulating-software-architectures},
tags = {chapter}
}
@inproceedings{krach2016b,
author = {Sebastian Krach and Christian Stier and Athanasios Tsitsipas},
series = {Softwaretechnik-Trends},
booktitle = {Proceedings of the Symposium on Software Performance (SSP) 2016},
title = {{Modeling IaaS Usage Patterns for the Analysis of Cloud Optimization Policies}},
publisher = {Gesellschaft f{\"u}r Informatik e.V.\ (GI)},
year = {2016},
month = {November},
tags = {refereed,workshop},
volume = {36(4)},
issn = {0720-8928},
pdf = {http://pi.informatik.uni-siegen.de/stt/36_4/01_Fachgruppenberichte/SSP2016/ssp-stt/29-Modeling_IaaS_Usage_Patterns_for_the_Analysis_of_Cloud_Optimization_Policies.pdf}
}
@inproceedings{WaStKoKo2017-QUDOS-PMXBuilder,
author = {Walter, J\"{u}rgen and Stier, Christian and Koziolek, Heiko and Kounev, Samuel},
title = {An Expandable Extraction Framework for Architectural Performance Models},
booktitle = {Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion},
series = {ICPE '17 Companion},
year = {2017},
isbn = {978-1-4503-4899-7},
location = {L'Aquila, Italy},
pages = {165--170},
numpages = {6},
url = {http://doi.acm.org/10.1145/3053600.3053634},
doi = {10.1145/3053600.3053634},
acmid = {3053634},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {automated performance model extraction, builder pattern, descartes modeling language, palladio component model}
}
@inproceedings{stier2017a,
author = {Christian Stier and Dominik Werle and Anne Koziolek},
title = {Deriving Power Models for Architecture-Level Energy Efficiency Analyses},
year = {2017},
publisher = {Springer International Publishing},
editor = {Reinecke, Philipp and Di Marco, Antinisca},
booktitle = {Computer Performance Engineering: 14th European Workshop, EPEW 2017, Berlin, Germany, September 7-8, 2017, Proceedings},
address = {Cham},
pages = {214--229},
abstract = {In early design phases and during software evolution, design-time energy efficiency analyses enable software architects to reason on the effect of design decisions on energy efficiency. Energy efficiency analyses rely on accurate power models to estimate power consumption. Deriving power models that are both accurate and usable for design time predictions requires extensive measurements and manual analysis. Existing approaches that aim to automate the extraction of power models focus on the construction of models for runtime estimation of power consumption. Power models constructed by these approaches do not allow users to identify the central set of system metrics that impact energy efficiency prediction accuracy. The identification of these central metrics is important for design time analyses, as an accurate prediction of each metric incurs modeling effort. We propose a methodology for the automated construction of multi-metric power models using systematic experimentation. Our approach enables the automated training and selection of power models for the design time prediction of power consumption. We validate our approach by evaluating the prediction accuracy of derived power models for a set of enterprise and data-intensive application benchmarks.},
isbn = {978-3-319-66583-2},
doi = {10.1007/978-3-319-66583-2_14},
url = {https://doi.org/10.1007/978-3-319-66583-2_14},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/stier2017a.pdf}
}
@inproceedings{stier2017b,
author = {Christian Stier and Anne Koziolek},
title = {Considering Transient Effects of Self-Adaptations in Model-Driven Performance Analyses},
booktitle = {Software Engineering 2017, Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-24. Februar 2017, Hannover, Deutschland},
pages = {99--100},
year = {2017},
url = {https://www.gi.de/fileadmin/redaktion/2017_LNI/lni-p-267-komplett.pdf}
}
@inproceedings{stier2018a,
title = {{Rapid Testing of IaaS Resource Management Algorithms via Cloud Middleware Simulation}},
titleaddon = {(Short Paper)},
author = {Christian Stier and J\"{o}rg Domaschka and Anne Koziolek and Sebastian Krach and Jakub Krzywda and Ralf Reussner},
booktitle = {Proceedings of the 9th ACM/SPEC International Conference on Performance Engineering},
series = {ICPE '18},
isbn = {978-1-4503-5095-2},
location = {Berlin, Germany},
publisher = {ACM},
address = {New York, NY, USA},
year = {2018},
numpages = {8},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/stier2018a.pdf},
pages = {184--191},
url = {http://doi.acm.org/10.1145/3184407.3184428},
doi = {10.1145/3184407.3184428},
keywords = {IaaS middleware simulation, cloud simulation, performance model extraction, performance simulation, power consumption prediction, simulation-based testing of resource management algorithms}
}
@inproceedings{ananieva2018b,
author = {Sofia Ananieva and Erik Burger and Christian Stier},
title = {{Model-Driven Consistency Preservation in AutomationML}},
booktitle = {14th IEEE International Conference on Automation Science and Engineering},
year = {2018},
location = {Munich, Germany},
publisher = {IEEE},
pages = {1536-1541},
doi = {10.1109/COASE.2018.8560343},
issn = {2161-8089},
pdf = {https://sdqweb.ipd.kit.edu/publications/pdfs/ananieva2018b.pdf},
tags = {Vitruv}
}