inproceedings_brosig.bib
@inproceedings{Br2011-SEDoktoranden-Symposium,
abstract = {{Today's enterprise systems based on increasingly complex software architectures often exhibit poor performance and resource efficiency thus having high operating costs. This is due to the inability to predict at run-time the effect of changes in the system environment and adapt the system accordingly. We propose a new performance modeling approach that allows the prediction of performance and system resource utilization online during system operation. We use architecture-level performance models that capture the performance-relevant information of the software architecture, deployment, execution environment and workload. The models will be automatically maintained during operation. To derive performance predictions, we propose a tailorable model solving approach to provide flexibility in view of prediction accuracy and analysis overhead.}},
address = {Bonn, Germany},
author = {Fabian Brosig},
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {Software Engineering (Workshops) - Doctoral Symposium, February 21--25, 2011},
editor = {Ralf Reussner and Alexander Pretschner and Stefan J{\"a}hnichen},
ee = {http://subs.emis.de/LNI/Proceedings/Proceedings184/article6310.html},
month = {February},
pages = {279--284},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/Br2011-SE-Symposium.pdf},
publisher = {GI},
series = {Lecture Notes in Informatics (LNI)},
title = {Online Performance Prediction with Architecture-Level Performance Models},
volume = {184},
year = {2011}
}
@inproceedings{BrHuKo2012-CBSE-ParamAndContextDep,
abstract = {Modern enterprise applications have to satisfy increasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on monitoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent.},
author = {Fabian Brosig and Nikolaus Huber and Samuel Kounev},
booktitle = {Proceedings of the 15th ACM SIGSOFT International Symposium on Component Based Software Engineering (CBSE 2012), June 26--28, 2012, Bertinoro, Italy},
month = {June},
note = {Acceptance Rate (Full Paper): 28.5\%.},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/BrHuKo2012-CBSE-ParamDep.pdf},
title = {{Modeling Parameter and Context Dependencies in Online Architecture-Level Performance Models}},
url = {http://cbse-conferences.org/2012/},
year = {2012}
}
@inproceedings{BrHuKo2011-ASE-AutomExtraction,
abstract = {Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the execution context of software components are not suitable for use at run-time. In this paper, we analyze the typical online performance prediction scenarios and propose a novel performance meta-model for expressing and resolving parameter and context dependencies, specifically designed for use in online scenarios. We motivate and validate our approach in the context of a realistic and representative online performance prediction scenario based on the SPECjEnterprise2010 standard benchmark.},
address = {Oread, Lawrence, Kansas},
author = {Fabian Brosig and Nikolaus Huber and Samuel Kounev},
booktitle = {26th IEEE/ACM International Conference On Automated Software Engineering (ASE 2011)},
month = {November},
note = {Acceptance Rate (Full Paper): 14.7\% (37/252)},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/BrHuKo2011-ASE-AutomExtraction.pdf},
title = {{A}utomated {E}xtraction of {A}rchitecture-{L}evel {P}erformance {M}odels of {D}istributed {C}omponent-{B}ased {S}ystems},
year = {2011}
}
@inproceedings{BrKoKr2009-ROSSA-Extracting_PCM_JavaEE,
abstract = {Nowadays, software systems have to fulfill increasingly stringent requirements for performance and scalability. To ensure that a system meets its performance requirements during operation, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of performance models of Java EE applications, based on monitoring data collected during operation. We extract instances of the Palladio Component Model (PCM) - a performance meta-model targeted at component-based systems. We evaluate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed.},
author = {Fabian Brosig and Samuel Kounev and Klaus Krogmann},
booktitle = {Proceedings of the 1st International Workshop on Run-time mOdels for Self-managing Systems and Applications (ROSSA 2009). In conjunction with the Fourth International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2009)},
month = {October},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/BrKoKr2009-ROSSA-Extracting_PCM_JavaEE.pdf},
publisher = {ACM, New York, NY, USA},
title = {{Automated Extraction of Palladio Component Models from Running Enterprise Java Applications}},
year = {2009},
isbn = {978-963-9799-70-7},
location = {Pisa, Italy},
pages = {10:1--10:10},
articleno = {10}
}
@inproceedings{FuBrFa2012-VALUETOOLS-TruthfulResourceReservation,
abstract = {{Prudent capacity planning to meet their clients future computational needs is one of the major issues cloud computing providers face today. By offering resource reservations in advance, providers gain insight into the projected demand of their customers and can act accordingly. However, customers need to be given an incentive, e.g. discounts granted, to commit early to a provider and to honestly, i.e., truthfully reserve their predicted future resource requirements. Customers may reserve capacity deviating from their truly predicted demand, in order to exploit the mechanism for their own benefit, thereby causing futile costs for the provider. In this paper we prove, using a game theoretic approach, that truthful reservation is the best, i.e., dominant strategy for customers if they are capable to make precise forecasts of their demands and that deviations from truth-telling can be profitable for customers if their demand forecasts are uncertain.}},
author = {Funke, Daniel and Brosig, Fabian and Faber, Michael},
booktitle = {{Proceedings of the 6th International ICST Conference on Performance Evaluation Methodologies and Tools (ValueTools 2012), Carg{\`e}se, France}},
month = {October},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/FuBrFa2012-VALUETOOLS-TruthfulResourceReservation.pdf},
title = {{Towards Truthful Resource Reservation in Cloud Computing}},
year = {2012}
}
@incollection{GiBrNoKoJu2012-ResBook-OnlinePrediction,
abstract = {{Current computing systems are becoming increasingly complex in nature and exhibit large variations in workloads. These changing environments create challenges to the design of systems that can adapt themselves while maintaining desired Quality of Service (QoS), security, dependability, availability and other non-functional requirements. The next generation of resilient systems will be highly distributed, component-based and service-oriented. They will need to operate in unattended mode and possibly in hostile environments, will be composed of a large number of interchangeable components discoverable at run-time, and will have to run on a multitude of unknown and heterogeneous hardware and network platforms. These computer systems will adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system, including security attacks and overwhelming situations. This self adaptation of next resilient systems can be achieved by first online predicting how these situations would be by observation of the current environment. This chapter focuses on the use of online predicting methods, techniques and tools for resilient systems. Thus, we survey online QoS adaptive models in several environments as grid environments, service-oriented architectures and ambient intelligence using different approaches based on queueing networks, model checking, ontology engineering among others.}},
address = {Berlin, Heidelberg},
author = {Katja Gilly and Fabian Brosig and Ramon Nou and Samuel Kounev and Carlos Juiz},
booktitle = {Resilience Assessment and Evaluation of Computing Systems},
editor = {K. Wolter and A. Avritzer and M. Vieira and A. van Moorsel},
isbn = {978-3-642-29031-2},
note = {ISBN: 978-3-642-29031-2},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/GiBrNoKoJu2012-ResBook-OnlinePredictionCaseStudies.pdf},
publisher = {Springer-Verlag},
series = {XVIII},
title = {Online prediction: Four case studies},
url = {http://www.springer.com/computer/communication+networks/book/978-3-642-29031-2},
year = {2012}
}
@incollection{HuBrDiJoKo2012-ResBook-CloudCaseStudies,
address = {Berlin, Heidelberg},
author = {Nikolaus Huber and Fabian Brosig and N. Dingle and K. Joshi and Samuel Kounev},
booktitle = {{Resilience Assessment and Evaluation of Computing Systems}},
editor = {K. Wolter and A. Avritzer and M. Vieira and A. van Moorsel},
isbn = {978-3-642-29031-2},
note = {ISBN: 978-3-642-29031-2},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuBrDiJoKo2012-ResBook-CloudCaseStudies.pdf},
publisher = {Springer-Verlag},
series = {XVIII},
title = {{Providing Dependability and Performance in the Cloud: Case Studies}},
url = {http://www.springer.com/computer/communication+networks/book/978-3-642-29031-2},
year = {2012}
}
@inproceedings{HuBrKo2012-QoSA-ModelingVirtResLandscapes,
abstract = {Modern data centers are subject to an increasing demand for flexibility. Increased flexibility and dynamics, however, also result in a higher system complexity. This complexity carries on to run-time resource management for Quality-of-Service (QoS) enforcement, rendering design-time approaches for QoS assurance inadequate. In this paper, we present a set of novel meta-models that can be used to describe the resource landscape, the architecture and resource layers of dynamic virtualized data center infrastructures, as well as their run-time adaptation and resource management aspects. With these meta-models we introduce new modeling concepts to improve model-based run-time QoS assurance. We evaluate our meta-models by modeling a representative virtualized service infrastructure and using these model instances for run-time resource allocation. The results demonstrate the benefits of the new meta-models and show how they can be used to improve model-based system adaptation and run-time resource management in dynamic virtualized data centers.},
address = {New York, NY, USA},
author = {Nikolaus Huber and Fabian Brosig and Samuel Kounev},
booktitle = {Proceedings of the 8th ACM SIGSOFT International Conference on the Quality of Software Architectures (QoSA 2012)},
day = {25--28},
doi = {10.1145/2304696.2304711},
isbn = {978-1-4503-1346-9},
location = {Bertinoro, Italy},
month = {June},
note = {Acceptance Rate (Full Paper): 25.6\%},
pages = {81--90},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuBrKo2012-QoSA-ModelingVirtResLandscapes.pdf},
publisher = {ACM},
title = {{Modeling Dynamic Virtualized Resource Landscapes}},
url = {http://qosa.ipd.kit.edu/qosa_2012/},
year = {2012}
}
@inproceedings{HuBrKo2011-SEAMS-ResAlloc,
abstract = {The adoption of virtualization and Cloud Computing technologies promises a number of benefits such as increased flexibility, better energy efficiency and lower operating costs for IT systems. However, highly variable workloads make it challenging to provide quality-of-service guarantees while at the same time ensuring efficient resource utilization. To avoid violations of service-level agreements (SLAs) or inefficient resource usage, resource allocations have to be adapted continuously during operation to reflect changes in application workloads. In this paper, we present a novel approach to self-adaptive resource allocation in virtualized environments based on online architecture-level performance models. We present a detailed case study of a representative enterprise application, the new SPECjEnterprise2010 benchmark, deployed in a virtualized cluster environment. The case study serves as a proof-of-concept demonstrating the effectiveness and practical applicability of our approach.},
address = {New York, NY, USA},
author = {Nikolaus Huber and Fabian Brosig and Samuel Kounev},
booktitle = {6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2011)},
day = {23--24},
doi = {10.1145/1988008.1988021},
isbn = {978-1-4503-0575-4},
location = {Waikiki, Honolulu, HI, USA},
month = {May},
note = {Acceptance Rate (Full Paper): 27\% (21/76)},
pages = {90--99},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuBrKo2011-SEAMS-ResAlloc.pdf},
publisher = {ACM},
title = {{Model-based Self-Adaptive Resource Allocation in Virtualized Environments}},
url = {http://dl.acm.org/authorize?425581},
year = {2011}
}
@inproceedings{HuHoKoBrKo2012-ICEBE-STA,
abstract = {Modern virtualized system environments usually host diverse applications of different parties and aim at utilizing resources efficiently while ensuring that quality-of-service requirements are continuously satisfied. In such scenarios, complex adaptations to changes in the system environment are still largely performed manually by humans. Over the past decade, autonomic self-adaptation techniques aiming to minimize human intervention have become increasingly popular. However, given that adaptation processes are usually highly system specific, it is a challenge to abstract from system details enabling the reuse of adaptation strategies. In this paper, we propose a novel modeling language (meta-model) providing means to describe system adaptation processes at the system architecture level in a generic, human-understandable and reusable way. We apply our approach to three different realistic contexts (dynamic resource allocation, software architecture optimization, and run-time adaptation planning) showing how the gap between complex manual adaptations and their autonomous execution can be closed by using a holistic model-based approach.},
address = {Los Alamitos, CA, USA},
author = {Nikolaus Huber and Andr\'{e} van Hoorn and Anne Koziolek and Fabian Brosig and Samuel Kounev},
booktitle = {Proceedings of the 9th IEEE International Conference on e-Business Engineering (ICEBE 2012)},
day = {9--11},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICEBE.2012.21},
isbn = {978-1-4673-2601-8},
location = {Hangzhou, China},
month = {September},
note = {Acceptance Rate (Full Paper): 19.7\% (26/132)},
pages = {70--77},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuHoKoBrKo2012-ICEBE-AdaptationLanguage.pdf},
publisher = {IEEE Computer Society},
title = {{S/T/A: Meta-Modeling Run-Time Adaptation in Component-Based System Architectures}},
url = {http://conferences.computer.org/icebe/2012/index.htm},
year = {2012}
}
@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{HuQuBrKo2010-DOA-AnalysisVirt,
abstract = {Nowadays, virtualization solutions are gaining increasing importance. By enabling the sharing of physical resources, thus making resource usage more efficient, they promise energy and cost savings. Additionally, virtualization is the key enabling technology for Cloud Computing and server consolidation. However, the effects of sharing resources on system performance are not yet well-understood. This makes performance prediction and performance management of services deployed in such dynamic systems very challenging. Because of the large variety of virtualization solutions, a generic approach to predict the performance influences of virtualization platforms is highly desirable. In this paper, we present a hierarchical model capturing the major performance-relevant factors of virtualization platforms. We then propose a general methodology to quantify the influence of the identified factors based on an empirical approach using benchmarks. Finally, we present a case study of Citrix XenServer 5.5, a state-of-the-art virtualization platform.},
address = {Crete, Greece},
author = {Nikolaus Huber and Marcel von Quast and Fabian Brosig and Samuel Kounev},
booktitle = {The 12th International Symposium on Distributed Objects, Middleware, and Applications (DOA 2010)},
day = {26},
location = {Crete, Greece},
month = {October},
note = {Acceptance Rate (Full Paper): 33\%},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuQuBrKo2010-DOA-AnalysisVirt.pdf},
publisher = {Springer Verlag},
title = {{Analysis of the Performance-Influencing Factors of Virtualization Platforms}},
year = {2010}
}
@inproceedings{KoBeBrHuOk2011-SIMUTools-DataFabrics,
abstract = {Enterprise data fabrics are gaining increasing attention in many industry domains including financial services, telecommunications, transportation and health care. Providing a distributed, operational data platform sitting between application infrastructures and back-end data sources, enterprise data fabrics are designed for high performance and scalability. However, given the dynamics of modern applications, system sizing and capacity planning need to be done continuously during operation to ensure adequate quality-of-service and efficient resource utilization. While most products are shipped with performance monitoring and analysis tools, such tools are typically focused on low-level profiling and they lack support for performance prediction and capacity planning. In this paper, we present a novel case study of a representative enterprise data fabric, the GemFire EDF, presenting a simulation-based tool that we have developed for automated performance prediction and capacity planning. The tool, called Jewel, automates resource demand estimation, performance model generation, performance model analysis and results processing. We present an experimental evaluation of the tool demonstrating its effctiveness and practical applicability.},
address = {Brussels, Belgium, Belgium},
author = {Samuel Kounev and Konstantin Bender and Fabian Brosig and Nikolaus Huber and Russell Okamoto},
booktitle = {4th International ICST Conference on Simulation Tools and Techniques},
day = {21--25},
isbn = {978-1-936968-00-8},
location = {Barcelona, Spain},
month = {March},
note = {Acceptance Rate (Full Paper): 29.8\% (23/77), ICST Best Paper Award},
pages = {27--36},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/KoBeBrHuOk2011-ICST-DataFabrics.pdf},
publisher = {ICST},
slides = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/KoBeBrHuOk2011-ICST-DataFabrics_Slides.pdf},
title = {{Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics}},
year = {2011}
}
@inproceedings{KoBrHu2011-ICAC-QoSManagement,
abstract = {We present an overview of our work-in-progress and long-term research agenda aiming to develop a novel methodology for engineering of self-aware software systems. The latter will have built-in architecture-level QoS models enhanced to capture dynamic aspects of the system environment and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and QoS requirements are satisfied.},
author = {Samuel Kounev and Fabian Brosig and Nikolaus Huber},
booktitle = {8th International Conference on Autonomic Computing (ICAC 2011)},
day = {14--18},
location = {Karlsruhe, Germany},
month = {June},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/KoBrHu2011-ICAC-QoSManagement.pdf},
title = {{Self-Aware QoS Management in Virtualized Infrastructures (Poster Paper)}},
year = {2011}
}
@inproceedings{KoBrHuRe2010-SCC-Towards,
abstract = {Modern service-oriented systems have increasingly complex loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment (e.g., varying service workloads) and adapt the system configuration accordingly. In this paper, we describe a long-term vision and approach for designing systems with built-in self-aware performance and resource management capabilities. We advocate the use of architecture-level performance models extracted dynamically from the evolving system configuration and maintained automatically during operation. The models will be exploited at run-time to adapt the system to changes in the environment ensuring that resources are utilized efficiently and performance requirements are continuously satisfied.},
author = {Samuel Kounev and Fabian Brosig and Nikolaus Huber and Ralf Reussner},
booktitle = {Proceedings of the 7th IEEE International Conference on Services Computing (SCC 2010), July 5-10, Miami, Florida, USA},
day = {5--10},
location = {Miami, Florida, USA},
month = {July},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/KoBrHuRe2010-SCC-Towards.pdf},
publisher = {IEEE Computer Society},
title = {{Towards self-aware performance and resource management in modern service-oriented systems}},
year = {2010}
}
@incollection{KoHuSpBr2012-business-inf-sys,
abstract = {{With the increasing adoption of virtualization and the transition towards Cloud Computing platforms, modern business information systems are becoming increasingly complex and dynamic. This raises the challenge of guaranteeing system performance and scalability while at the same time ensuring efficient resource usage. In this paper, we present a historical perspective on the evolution of model-based performance engineering techniques for business information systems focusing on the major developments over the past several decades that have shaped the field. We survey the state-of-the-art on performance modeling and management approaches discussing the ongoing efforts in the community to increasingly bridge the gap between high-level business services and low level performance models. Finally, we wrap up with an outlook on the emergence of self-aware systems engineering as a new research area at the intersection of several computer science disciplines.}},
address = {Berlin, Heidelberg},
author = {Samuel Kounev and Nikolaus Huber and Simon Spinner and Fabian Brosig},
booktitle = {Business Modeling and Software Design},
editor = {Shishkov, Boris},
isbn = {978-3-642-29788-5},
pages = {19--37},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/KoHuSpBr2012-business-inf-sys.pdf},
publisher = {Springer-Verlag},
series = {Lecture Notes in Business Information Processing (LNBIP)},
title = {Model-based Techniques for Performance Engineering of Business Information Systems},
url = {http://dx.doi.org/10.1007/978-3-642-29788-5_2},
volume = {0109},
year = {2012}
}
@incollection{KoReBrBrJoBaStGi2012-ResBook-CloudChallenges,
abstract = {{Cloud Computing is a novel paradigm for providing data center resources as on demand services in a pay-as-you-go manner. It promises significant cost savings by making it possible to consolidate workloads and share infrastructure resources among multiple applications resulting in higher cost- and energy-efficiency. However, these benefits come at the cost of increased system complexity and dynamicity posing new challenges in providing service dependability and resilience for applications running in a Cloud environment. At the same time, the virtualization of physical resources, inherent in Cloud Computing, provides new opportunities for novel dependability and quality-of-service management techniques that can potentially improve system resilience. In this chapter, we first discuss in detail the challenges and opportunities introduced by the Cloud Computing paradigm. We then provide a review of the state-of-the-art on dependability and resilience management in Cloud environments, and conclude with an overview of emerging research directions.}},
address = {Berlin, Heidelberg},
author = {Samuel Kounev and Philipp Reinecke and Fabian Brosig and Jeremy T. Bradley and Kaustubh Joshi and Vlastimil Babka and Anton Stefanek and Stephen Gilmore},
booktitle = {Resilience Assessment and Evaluation of Computing Systems},
editor = {K. Wolter and A. Avritzer and M. Vieira and A. van Moorsel},
isbn = {978-3-642-29031-2},
note = {ISBN: 978-3-642-29031-2},
pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/KoReBrBrJoBaStGi2012-ResBook-CloudChallenges.pdf},
publisher = {Springer-Verlag},
series = {XVIII},
title = {Providing Dependability and Resilience in the Cloud: Challenges and Opportunities},
url = {http://www.springer.com/computer/communication+networks/book/978-3-642-29031-2},
year = {2012}
}
@inproceedings{BrGoHuKo2013-MASCOTS-EvaluationApproachesForPerformancePredictionInVirtualizedEnvironments,
author = {Fabian Brosig and Fabian Gorsler and Nikolaus Huber and Samuel Kounev},
booktitle = {Proceedings of the IEEE 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS 2013)},
day = {14--16},
location = {San Francisco, USA},
month = {August},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/BrGoHuKo2013-MASCOTS-EvaluationApproachesForPerformancePredictionInVirtualizedEnvironments.pdf},
title = {{Evaluating Approaches for Performance Prediction in Virtualized Environments}},
titleaddon = {{(Short Paper)}},
year = {2013}
}
@inproceedings{GoBrKo2013-ControllingPCM,
abstract = {The Palladio Bench is a tool to model, simulate and analyze Palladio Component Model (PCM) instances. However, for the Palladio Bench, no single interface to automate experiments or Application Programming Interface (API) to trigger the simulation of PCM instances and to extract performance prediction results is available. The Descartes Query Language (DQL) is a novel approach of a declarative query language to integrate different performance modeling and prediction techniques behind a unifying interface. Users benefit from the abstraction of specific tools to prepare and trigger performance predictions, less effort to obtain performance metrics of interest, and means to automate performance predictions. In this paper, we describe the realization of a DQL Connector for PCM and demonstrate the applicability of our approach in a case study.},
address = {Aachen, Germany},
author = {Gorsler, Fabian and Brosig, Fabian and Kounev, Samuel},
booktitle = {Proceedings of the Symposium on Software Performance: Joint Kieker/Palladio Days (KPDAYS 2013)},
editor = {Becker, Steffen and Hasselbring, Wilhelm and van Hoorn, Andr\'{e} and Reussner, Ralf},
month = {November},
number = {1083},
pages = {109--118},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/GoBrKo2013-ControllingPCM.pdf},
publisher = {CEUR-WS.org},
title = {Controlling the Palladio Bench using the Descartes Query Language},
url = {http://ceur-ws.org/Vol-1083/},
year = {2013}
}
@inproceedings{GoBrKo2014-PerformanceQueries,
address = {New York, NY, USA},
author = {Gorsler, Fabian and Brosig, Fabian and Kounev, Samuel},
booktitle = {Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
location = {Dublin, Ireland},
note = {Accepted for publication. Acceptance Rate (Full Paper): 29\%.},
publisher = {ACM},
title = {Performance Queries for Architecture-Level Performance Models},
year = {2014}
}
@incollection{happe2016a,
author = {Jens Happe and Benjamin Klatt and Martin K\"{u}ster and Fabian Brosig and Alexander Wert and Simon Spinner and Heiko Koziolek},
title = {Getting the Data},
pages = {115--138},
chapter = {6},
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{kounev2016analysis,
title = {Analysis of the trade-offs in different modeling approaches for performance prediction of software systems.},
author = {Kounev, Samuel and Brosig, Fabian and Meier, Philipp and Becker, Steffen and Koziolek, Anne and Koziolek, Heiko and Rygielski, Piotr},
booktitle = {Software Engineering},
pages = {47--48},
year = {2016}
}