2014.bib

@article{HuKoAm2013-CCPE-WorkloadClassificationAndForecasting,
  abstract = {As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis covers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel self-adaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on real world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.},
  author = {Nikolas Roman Herbst and Nikolaus Huber and Samuel Kounev and Erich Amrehn},
  doi = {10.1002/cpe.3224},
  issn = {1532-0634},
  journal = {Concurrency and Computation - Practice and Experience, Special Issue with extended versions of the best papers from ICPE 2013, John Wiley and Sons, Ltd.},
  keywords = {workload forecasting, arrival rate, time series analysis, proactive resource provisioning, assurance of service level objectives},
  title = {{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}},
  url = {http://dx.doi.org/10.1002/cpe.3224},
  year = {2014}
}

@inproceedings{KiHeKo2014-LT-DLIM,
  abstract = {{Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, we identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The High-Level Descartes Load Intensity Meta-Model (HLDLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts and noise parts. We demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy through comparison with a real life trace.}},
  acmid = {2577037},
  address = {New York, NY, USA},
  author = {J\'{o}akim Gunnarson von Kistowski and Nikolas Roman Herbst and Samuel Kounev},
  booktitle = {Proceedings of the 3rd International Workshop on Large-Scale Testing (LT 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
  day = {22},
  doi = {10.1145/2577036.2577037},
  isbn = {978-1-4503-2762-6},
  keywords = {benchmarking, modeling, workload},
  location = {Dublin, Ireland},
  month = {March},
  numpages = {4},
  pages = {1--4},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/KiHeKo2014-LT-DLIM.pdf},
  publisher = {ACM},
  slides = {http://lt2014.eecs.yorku.ca/talks/Joakim_LTslides.pdf},
  title = {{Modeling Variations in Load Intensity over Time}},
  url = {http://doi.acm.org/10.1145/2577036.2577037},
  year = {2014}
}

@inproceedings{KiHeKo2014-ICPEDemo-LIMBO,
  abstract = {{Modern software systems are expected to deliver reliable performance under highly variable load 	intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. In this paper, we present LIMBO - an Eclipse-based tool for modeling variable load intensity profiles based on the Descartes Load Intensity Model as an underlying modeling formalism.}},
  acmid = {2576092},
  address = {New York, NY, USA},
  author = {J\'{o}akim Gunnarson von Kistowski and Nikolas Roman Herbst and Samuel Kounev},
  booktitle = {Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
  day = {22--26},
  doi = {10.1145/2568088.2576092},
  isbn = {978-1-4503-2733-6},
  keywords = {load intensity variation, load profile, meta-modeling, model extraction, open workloads, transformation},
  location = {Dublin, Ireland},
  month = {March},
  numpages = {2},
  pages = {225--226},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/KiHeKo2014-ICPEDemo-LIMBO.pdf},
  publisher = {ACM},
  series = {ICPE '14},
  slides = {http://sdqweb.ipd.kit.edu/publications/pdfs/KiHeKo2014-ICPEDemo-LIMBO-Poster.pdf},
  title = {{LIMBO: A Tool For Modeling Variable Load Intensities}},
  titleaddon = {{(Demonstration Paper)}},
  url = {http://doi.acm.org/10.1145/2568088.2576092},
  year = {2014}
}

@inproceedings{WeHeGrKo2014-HotTopicsWS-ElaBench,
  abstract = {{Auto-scaling features offered by today's cloud infrastructures provide increased flexibility especially for customers that experience high variations in the load intensity over time. However, auto-scaling features introduce new system quality attributes when considering their accuracy, timing, and boundaries. Therefore, distinguishing between different offerings has become a complex task, as it is not yet supported by reliable metrics and measurement approaches. In this paper, we discuss shortcomings of existing approaches for measuring and evaluating elastic behavior and propose a novel benchmark methodology specifically designed for evaluating the elasticity aspects of modern cloud platforms. The benchmark is based on open workloads with realistic load variation profiles that are calibrated to induce identical resource demand variations independent of the underlying hardware performance. Furthermore, we propose new metrics that capture the accuracy of resource allocations and de-allocations, as well as the timing aspects of an auto-scaling mechanism explicitly.}},
  author = {Andreas Weber and Nikolas Roman Herbst and Henning Groenda and Samuel Kounev},
  booktitle = {Proceedings of the 2nd International Workshop on Hot Topics in Cloud Service Scalability (HotTopiCS 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
  day = {22},
  keywords = {benchmarking, metrics, cloud computing, resource elasticity, load profile},
  location = {Dublin, Ireland},
  month = {March},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/WeHeGrKo2014-HotTopicsWS-ElaBench.pdf},
  publisher = {ACM},
  slides = {http://sdqweb.ipd.kit.edu/publications/pdfs/WeHeGrKo2014-HotTopicsWS-ElaBench-Slides.pdf},
  title = {{Towards a Resource Elasticity Benchmark for Cloud Environments}},
  year = {2014}
}

@techreport{KoBrHu2014-TechReport-DMM,
  abstract = {{This technical report introduces the Descartes Modeling Language (DML), a new architecture-level modeling language for modeling Quality-of-Service (QoS) and resource management related aspects of modern dynamic IT systems, infrastructures and services. DML is designed to serve as a basis for self-aware resource management during operation ensuring that system QoS requirements are continuously satisfied while infrastructure resources are utilized as efficiently as possible.}},
  author = {Samuel Kounev and Fabian Brosig and Nikolaus Huber},
  http = {http://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/10488},
  institution = {{Department of Computer Science, University of Wuerzburg}},
  month = {October},
  pages = {91},
  pdf = {http://opus.bibliothek.uni-wuerzburg.de/files/10488/DML-TechReport-1.0.pdf},
  title = {{The Descartes Modeling Language}},
  url = {http://www.descartes-research.net/dml/},
  year = {2014}
}

@article{becker2014c,
  author = {Becker, Steffen and Hasselbring, Wilhelm and van Hoorn, Andre and Kounev, Samuel and Reussner, Ralf and others},
  publisher = {Stuttgart, Germany, Universit{\"a}t Stuttgart},
  title = {Proceedings of the 2014 Symposium on Software Performance (SOSP'14): Joint Descartes/Kieker/Palladio Days},
  year = {2014}
}

@inproceedings{martinec2014a,
  acmid = {2568096},
  address = {New York, NY, USA},
  author = {Martinec, Tom\'{a}\c{s} and Marek, Luk\'{a}\c{s} and Steinhauser, Anton\'{\i}n and T\r{u}ma, Petr and Noorshams, Qais and Rentschler, Andreas and Reussner, Ralf},
  booktitle = {Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering},
  doi = {10.1145/2568088.2568096},
  isbn = {978-1-4503-2733-6},
  keywords = {JMS, measurement, modeling, performance analysis, software performance},
  location = {Dublin, Ireland},
  numpages = {12},
  pages = {123--134},
  publisher = {ACM},
  series = {ICPE '14},
  title = {Constructing Performance Model of JMS Middleware Platform},
  url = {http://doi.acm.org/10.1145/2568088.2568096},
  year = {2014}
}

@inproceedings{noorshams2014c,
  author = {Qais Noorshams and Kiana Rostami and Samuel Kounev and Ralf Reussner},
  booktitle = {Proceedings of the IEEE 22nd International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems},
  date = {September 09--11},
  location = {France, Paris},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/noorshams2014c.pdf},
  series = {MASCOTS '14},
  title = {{Modeling of I/O Performance Interference in Virtualized Environments with Queueing Petri Nets}},
  tags = {refereed},
  year = {2014}
}

@inproceedings{noorshams2014b,
  acmid = {2602475},
  address = {New York, NY, USA},
  author = {Noorshams, Qais and Reeb, Roland and Rentschler, Andreas and Kounev, Samuel and Reussner, Ralf},
  booktitle = {Proceedings of the 17th International ACM Sigsoft Symposium on Component-based Software Engineering},
  doi = {10.1145/2602458.2602475},
  isbn = {978-1-4503-2577-6},
  keywords = {i/o, performance, prediction, software architecture, statistical model, storage},
  location = {Marcq-en-Bareul, France},
  note = {Acceptance Rate (Full Paper): 14/62 = 23\%.},
  numpages = {10},
  pages = {45--54},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/noorshams2014b.pdf},
  publisher = {ACM},
  series = {CBSE '14},
  title = {Enriching Software Architecture Models with Statistical Models for Performance Prediction in Modern Storage Environments},
  url = {http://doi.acm.org/10.1145/2602458.2602475},
  year = {2014}
}

@inproceedings{noorshams2014a,
  author = {Qais Noorshams and Axel Busch and Andreas Rentschler and Dominik Bruhn and Samuel Kounev and Petr T\r{u}ma and Ralf Reussner},
  booktitle = {34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Workshops). 4th International Workshop on Data Center Performance, DCPerf '14},
  doi = {10.1109/ICDCSW.2014.26},
  location = {Madrid, Spain},
  pages = {88-93},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/noorshams2014a.pdf},
  title = {{Automated Modeling of I/O Performance and Interference Effects in Virtualized Storage Systems}},
  url = {http://dx.doi.org/10.1109/ICDCSW.2014.26},
  year = {2014}
}

@inproceedings{rentschler2014a,
  acmid = {2577094},
  address = {New York, NY, USA},
  author = {Andreas Rentschler and Dominik Werle and Qais Noorshams and Lucia Happe and Ralf Reussner},
  booktitle = {Proceedings of the 13th International Conference on Modularity (AOSD '14), Lugano, Switzerland, April 22 - 26, 2014},
  doi = {10.1145/2577080.2577094},
  isbn = {978-1-450-32772-5},
  month = {April},
  note = {Acceptance Rate: 35.0\%},
  numpages = {12},
  pages = {217--228},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/rentschler2014a.pdf},
  publisher = {ACM},
  title = {{Designing Information Hiding Modularity for Model Transformation Languages}},
  url = {http://doi.acm.org/10.1145/2577080.2577094},
  year = {2014}
}

@inproceedings{rentschler2014b,
  _booktitle = {Proceedings of the 3rd Workshop on the Analysis of Model Transformations (AMT@MODELS 2014), Valencia, Spain, September 29, 2014},
  _pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/rentschler2014b.pdf},
  author = {Andreas Rentschler and Dominik Werle and Qais Noorshams and Lucia Happe and Ralf Reussner},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {Proceedings of the 3rd Workshop on the Analysis of Model Transformations co-located with the 17th International Conference on Model Driven Engineering Languages and Systems (AMT{}@{}MOD\-ELS '14), Valencia, Spain, September 29, 2014},
  editor = {Benoit Baudry and J{\"u}rgen Dingel and Levi Lucio and Hans Vangheluwe},
  issn = {1613-0073},
  month = {October},
  pages = {4--13},
  pdf = {http://ceur-ws.org/Vol-1277/1.pdf},
  publisher = {CEUR-WS.org},
  series = {CEUR Workshop Proceedings},
  title = {{Remodularizing Legacy Model Transformations with Automatic Clustering Techniques}},
  url = {http://nbn-resolving.de/urn:nbn:de:0074-1277-5},
  volume = {1277},
  year = {2014}
}

@article{KrMoKo2013-SciCo-MetricsAndTechniquesForPerformanceIsolation,
  author = {Rouven Krebs and Christof Momm and Samuel Kounev},
  journal = {Elsevier Science of Computer Programming Journal (SciCo)},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/KrMoKo2013-SciCo-MetricsAndTechniquesForPerformanceIsolation.pdf},
  publisher = {Elsevier B.V.},
  title = {{Metrics and Techniques for Quantifying Performance Isolation in Cloud Environments}},
  volume = {90, Part B},
  number = {},
  pages = {116 - 134},
  year = {2014},
  note = {Special Issue on Component-Based Software Engineering and Software Architecture},
  issn = {0167-6423},
  doi = {http://dx.doi.org/10.1016/j.scico.2013.08.003},
  url = {http://www.sciencedirect.com/science/article/pii/S0167642313001962},
  abstract = {The cloud computing paradigm enables the provision of cost efficient IT-services by leveraging economies of scale and sharing data center resources efficiently among multiple independent applications and customers. However, the sharing of resources leads to possible interference between users and performance problems are one of the major obstacles for potential cloud customers. Consequently, it is one of the primary goals of cloud service providers to have different customers and their hosted applications isolated as much as possible in terms of the performance they observe. To make different offerings, comparable with regards to their performance isolation capabilities, a representative metric is needed to quantify the level of performance isolation in cloud environments. Such a metric should allow to measure externally by running benchmarks from the outside treating the cloud as a black box. In this article, we propose three different types of novel metrics for quantifying the performance isolation of cloud-based systems. We consider four new approaches to achieve performance isolation in Software-as-a-Service (SaaS) offerings and evaluate them based on the proposed metrics as part of a simulation-based case study. To demonstrate the effectiveness and practical applicability of the proposed metrics for quantifying the performance isolation in various scenarios, we present a second case study evaluating performance isolation of the hypervisor Xen.}
}

@article{BrHuKo2013-SciCo-SoftwarePerformanceAbstractions,
  author = {Fabian Brosig and Nikolaus Huber and Samuel Kounev},
  doi = {10.1016/j.scico.2013.06.004},
  journal = {Elsevier Science of Computer Programming Journal (SciCo)},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/BrHuKo2013-SciCo-SoftwarePerformanceAbstractions.pdf},
  publisher = {Elsevier},
  title = {{Architecture-Level Software Performance Abstractions for Online Performance Prediction}},
  url = {http://authors.elsevier.com/sd/article/S0167642313001421},
  year = {2014},
  volume = {90, Part B},
  pages = {71 - 92},
  year = {2014},
  issn = {0167-6423}
}

@article{HuHoKoBrKo2014-SOCA-ModelingRuntimeAdaptation,
  author = {Nikolaus Huber and Andr\'{e} van Hoorn and Anne Koziolek and Fabian Brosig and Samuel Kounev},
  doi = {10.1007/s11761-013-0144-4},
  journal = {Service Oriented Computing and Applications Journal (SOCA)},
  number = {1},
  pages = {73--89},
  pdf = {http://sdqweb.ipd.kit.edu/publications/descartes-pdfs/HuHoKoBrKo2013-SOCA-ModelingRuntimeAdaptation.pdf},
  publisher = {Springer London},
  title = {{Modeling Run-Time Adaptation at the System Architecture Level in Dynamic Service-Oriented Environments}},
  volume = {8},
  year = {2014},
  tags = {peer-reviewed}
}

@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}
}

@inproceedings{SpCaZhKo2014-ICPEDemo-LibReDE,
  abstract = {When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.},
  author = {Simon Spinner and Giuliano Casale and Xiaoyun Zhu and Samuel Kounev},
  booktitle = {Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
  day = {22--26},
  location = {Dublin, Ireland},
  month = {March},
  note = {Accepted for Publication},
  publisher = {ACM},
  title = {{LibReDE: A Library for Resource Demand Estimation}},
  titleaddon = {{(Demonstration Paper)}},
  year = {2014}
}

@inproceedings{RyKo2014-DCPerf-DNI2QPN,
  author = {Piotr Rygielski and Samuel Kounev},
  booktitle = {34th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2014 Wokrshops). 4th International Workshop on Data Center Performance, (DCPerf 2014)},
  location = {Madrid, Spain},
  note = {(Paper accepted for publication)},
  title = {{Data Center Network Throughput Analysis using Queueing Petri Nets}},
  year = {2014}
}

@inproceedings{KrScHe2014-HotTopiCS-OptimizationApproach,
  abstract = {{Software-as-a-Service (SaaS) often shares one single application instance among different tenants to reduce costs. However, sharing potentially leads to undesired influence from one tenant onto the performance observed by the others. Furthermore, providing one tenant additional resources to support its increasing demands without increasing the performance of tenants who do not pay for it is a major challenge. The application intentionally does not manage hardware resources, and the OS is not aware of application level entities like tenants. Thus, it is difficult to control the performance of different tenants to keep them isolated. These problems gain importance as performance is one of the major obstacles for cloud customers. Existing work applies request based admission control mechanisms like a weighted round robin with an individual queue for each tenant to control the share guaranteed for a tenant. However, the computation of the concrete weights for such an admission control is still challenging. In this paper, we present a fitness function and optimization approach reflecting various requirements from this field to compute proper weights with the goal to ensure an isolated performance as foundation to scale on a tenants basis.}},
  author = {Rouven Krebs and Philipp Schneider and Nikolas Herbst},
  booktitle = {Proceedings of the 2nd International Workshop on Hot Topics in Cloud Service Scalability (HotTopiCS 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
  day = {22},
  keywords = {SaaS, Multi-Tenancy, Performance, Isolation, Scalability},
  location = {Dublin, Ireland},
  month = {March},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/KrScHe2014-HotTopiCS-OptimizationApproach.pdf},
  publisher = {ACM},
  slides = {http://sdqweb.ipd.kit.edu/publications/pdfs/KrScHe2014-HotTopiCS-OptimizationApproach-Slides.pdf},
  title = {{Optimization Method for Request Admission Control to Guarantee Performance Isolation}},
  year = {2014}
}

@inproceedings{KrSpAhKo2014_CCGrid_ResourceIsolation,
  abstract = {{Multi-tenancy is an approach to share one application instance among multiple customers by providing each of them a dedicated view. This approach is commonly used by SaaS providers to reduce the costs for service provisioning. Tenants also expect to be isolated in terms of the performance they observe and the providers inability to offer performance guarantees is a major obstacle for potential cloud customers. To guarantee an isolated performance it is essential to control the resources used by a tenant. This is a challenge, because the layers of the execution environment, responsible for controlling resource usage (e.g., operating system), normally do not have knowledge about entities defined at the application level and thus they cannot distinguish between different tenants. Furthermore, it is hard to predict how tenant requests propagate through the multiple layers of the execution environment down to the physical resource layer. The intended abstraction of the application from the resource controlling layers does not allow to solely solving this problem in the application. In this paper, we propose an approach which applies resource demand estimation techniques in combination with a request based admission control. The resource demand estimation is used to determine resource consumption information for individual requests. The admission control mechanism uses this knowledge to delay requests originating from tenants that exceed their allocated resource share. The proposed method is validated by a widely accepted benchmark showing its applicability in a setup motivated by today's platform environments.}},
  author = {Rouven Krebs and Simon Spinner and Nadia Ahmed and Samuel Kounev},
  booktitle = {Proceedings of the 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014)},
  day = {26},
  location = {Chicago, IL, USA},
  month = {May},
  note = {Accepted for Publication},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/KrSpAhKo2014_CCGrid_ResourceIsolation.pdf},
  publisher = {IEEE/ACM},
  title = {{Resource Usage Control In Multi-Tenant Applications}},
  year = {2014}
}

@inproceedings{KrLo2014_Closer_IsolationTypes,
  abstract = {{Multi-tenancy is an approach to share one application instance among multiple customers by providing each of them a dedicated view. This approach is commonly used by SaaS providers to reduce the costs for service provisioning. Tenants also expect to be isolated in terms of the performance they observe and the providers inability to offer performance guarantees is a major obstacle for potential cloud customers. To guarantee an isolated performance it is essential to control the resources used by a tenant. This is a challenge, because the layers of the execution environment, responsible for controlling resource usage (e.g., operating system), normally do not have knowledge about entities defined at the application level and thus they cannot distinguish between different tenants. Furthermore, it is hard to predict how tenant requests propagate through the multiple layers of the execution environment down to the physical resource layer. The intended abstraction of the application from the resource controlling layers does not allow to solely solving this problem in the application. In this paper, we propose an approach which applies resource demand estimation techniques in combination with a request based admission control. The resource demand estimation is used to determine resource consumption information for individual requests. The admission control mechanism uses this knowledge to delay requests originating from tenants that exceed their allocated resource share. The proposed method is validated by a widely accepted benchmark showing its applicability in a setup motivated by today's platform environments.}},
  author = {Rouven Krebs and Manuel Loesch},
  booktitle = {Proceedings of 4th International Conference On Cloud Computing And Services Science (CLOSER 2014)},
  day = {3},
  location = {Barcelona, Spain},
  month = {April},
  note = {Short Paper},
  pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/KrLo2014_Closer_IsolationTypes.pdf},
  publisher = {SciTePress},
  title = {{Comparison of Request Admission Based Performance Isolation Approaches in Multi-Tenant SaaS Applications}},
  year = {2014}
}