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Publications of Christian Stier

Refereed Conference/Workshop papers

[1] Jürgen Walter, Christian Stier, Heiko Koziolek, and Samuel Kounev. An Expandable Extraction Framework for Architectural Performance Models. In Proceedings of the 3rd International Workshop on Quality-Aware DevOps (QUDOS'17), l'Aquila, Italy, April 2017. ACM. April 2017, To Appear. [ bib ]
[2] Sebastian Krach, Christian Stier, and Athanasios Tsitsipas. Modeling iaas usage patterns for the analysis of cloud optimization policies. In Proceedings of the Symposium on Software Performance (SSP) 2016, November 2016, Softwaretechnik-Trends. to appear. [ bib | .pdf ]
[3] Sebastian Lehrig, Steffen Becker, Christian Stier, and Ralf H. Reussner. Future trends. In Modeling and Simulating Software Architectures - The Palladio Approach, Ralf H. Reussner, Steffen Becker, Jens Happe, Robert Heinrich, Anne Koziolek, Heiko Koziolek, Max Kramer, and Klaus Krogmann, editors, chapter 15, pages 339-342. MIT Press, Cambridge, MA, October 2016. [ bib | http ]
[4] Christian Stier and Anne Koziolek. Considering Transient Effects of Self-Adaptations in Model-Driven Performance Analyses. In 2016 12th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA), Venice, Italy, 2016, QoSA'16. ACM. 2016. [ bib | DOI | 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.
[5] Christian Stier and Henning Groenda. Ensuring Model Continuity when Simulating Self-Adaptive Software Systems. In 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), Pasadena, CA, USA, 2016, MSCIAAS, pages 2:1-2:8. Society for Computer Simulation International. 2016. [ bib | http | 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.
[6] Sergej Svorobej, James Byrne, Paul Liston, PJ Byrne, Christian Stier, Henning Groenda, Zafeirios Papazachos, and Dimitrios Nikolopoulos. Towards automated data-driven model creation for cloud computing simulation. In Eighth EAI International Conference on Simulation Tools and Techniques (SIMUTOOLS), August 2015. ACM. August 2015. [ bib | DOI ]
[7] Christian Stier, Anne Koziolek, Henning Groenda, and Ralf Reussner. Model-Based Energy Efficiency Analysis of Software Architectures. In Proceedings of the 9th European Conference on Software Architecture (ECSA '15), Dubrovnik/Cavtat, Croatia, 2015, Lecture Notes in Computer Science. Springer. 2015, Acceptance Rate (Full Paper): 15/80 = 18.8%. [ bib | DOI | http | .pdf | 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.
[8] Henning Groenda and Christian Stier. Improving IaaS Cloud Analyses by Black-Box Resource Demand Modeling. In Symposium on Software Performance 2015, 2015. [ bib | .pdf ]
[9] Philipp Merkle and Christian Stier. Modelling Database Lock-Contention in Architecture-level Performance Simulation. In Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering, Dublin, Ireland, 2014, ICPE '14. ACM, New York, NY, USA. 2014, Work-In-Progress Paper. [ bib ]
[10] P-O Östberg, Henning Groenda, Stefan Wesner, James Byrne, Dimitrios S. Nikolopoulos, Craig Sheridan, Jakub Krzywda, Ahmed Ali-Eldin, Johan Tordsson, Erik Elmroth, Christian Stier, Klaus Krogmann, Jörg Domaschka, Christopher Hauser, PJ Byrne, Sergej Svorobej, Barry McCollum, Zafeirios Papazachos, Loke Johannessen, Stephan Rüth, and Dragana Paurevic. The CACTOS Vision of Context-Aware Cloud Topology Optimization and Simulation. In Proceedings of the Sixth IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2014, pages 26-31. IEEE Computer Society, Singapore. 2014. [ bib | DOI ]
[11] Andranik Khachatryan, Emmanuel Müller, Christian Stier, and Klemens Böhm. Sensitivity of Self-tuning Histograms: Query Order Affecting Accuracy and Robustness. In Proceedings of the 24th International Conference on Scientific and Statistical Database Management, Chania, Crete, Greece, 2012, SSDBM, pages 334-342. Springer-Verlag, Berlin, Heidelberg. 2012. [ bib | DOI | http ]

Refereed Journal Articles

[1] Jóakim von Kistowski, Nikolas Herbst, Samuel Kounev, Henning Groenda, Christian Stier, and Stebastian Lehrig. Modeling and Extracting Load Intensity Profiles. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2017, ACM, New York, NY, USA. To Appear. [ bib | Abstract ]
Today's system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Autonomic controllers, e.g., an advanced auto-scaling mechanism in a cloud computing context, can benefit from an abstracted load model as knowledge to reconfigure on time and precisely. Existing workload characterization approaches have limited support to capture variations the inter-arrival times of incoming work units over time (i.e., a variable load profile). For example, industrial and scientific benchmarks support constant or stepwise increasing load, or inter-arrival times defined by statistical distributions or recorded traces. These options show shortcomings either in representative character of load variation patterns or in abstraction and flexibility of their format. In this article, we present the Descartes Load Intensity Model (DLIM) approach addressing these issues. DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance is a compact formal description of a load intensity trace. DLIM-based tools provide features for benchmarking, performance and recorded load intensity trace analysis. As manually obtaining and maintaining DLIM instances becomes time consuming, we contribute three automated extraction methods and devised metrics for comparison and method selection. We discuss how these features are used to enhance system management approaches for adaptations during run-time, and how they are integrated into simulation contexts and enable benchmarking of elastic or adaptive behavior. We show that automatically extracted DLIM instances exhibit an average modeling error of 15.2% over ten different real-world traces that cover between two weeks and seven months. These results underline DLIM model expressiveness. In terms of accuracy and processing speed, our proposed extraction methods for the descriptive models are comparable to existing time series decomposition methods. Additionally, we illustrate DLIM applicability by outlining approaches of workload modeling in systems engineering that employ or rely on our proposed load intensity modeling formalism.
[2] Andranik Khachatryan, Emmanuel Müller, Christian Stier, and Klemens Böhm. Improving accuracy and robustness of self-tuning histograms by subspace clustering. IEEE Transactions on Knowledge & Data Engineering, 27(9):2377-2389. [ bib ]

Technical Reports

[1] Christian Stier, Henning Groenda, and Anne Koziolek. Towards Modeling and Analysis of Power Consumption of Self-Adaptive Software Systems in Palladio. Technical report, University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, November 2014. [ bib | slides | .pdf ]


[1] Christian Stier. Transaction-Aware Software Performance Prediction. Master's thesis, Karlsruhe Institute of Technology (KIT), Am Fasanengarten 5, 76131 Karlsruhe, Germany, January 2014. [ bib | .pdf ]
[2] Christian Stier. Enhanced Selectivity Estimation using Subspace Clustering. Bachelor's thesis, Karlsruhe Institute of Technology (KIT), Am Fasanengarten 5, 76131 Karlsruhe, Germany, 2011. [ bib ]


[1] Christian Stier, Anne Koziolek, Henning Groenda, and Ralf Reussner. Model-based analysis of energy efficiency for software architectures. Poster at the Symposium on Software Performance 2015, 2015. Best Poster Award. [ bib ]