inproceedings_herbst.bib
@inproceedings{Herbst2015,
abstract = {Today's infrastructure clouds provide resource elasticity (i.e. auto-scaling) mechanisms enabling self-adaptive resource provisioning to reflect variations in the load intensity over time. These mechanisms impact on the application performance, however, their effect in specific situations is hard to quantify and compare. To evaluate the quality of elasticity mechanisms provided by different platforms and configurations, respective metrics and benchmarks are required. Existing metrics for elasticity only consider the time required to provision and deprovision resources or the costs impact of adaptations. Existing benchmarks lack the capability to handle open workloads with realistic load intensity profiles and do not explicitly distinguish between the performance exhibited by the provisioned underlying resources, on the one hand, and the quality of the elasticity mechanisms themselves, on the other hand. In this paper, we propose reliable metrics for quantifying the timing aspects and accuracy of elasticity. Based on these metrics, we propose a novel approach for benchmarking the elasticity of Infrastructure-as-a-Service (IaaS) cloud platforms independent of the performance exhibited by the provisioned underlying resources. We show that the proposed metrics provide consistent ranking of elastic platforms on an ordinal scale. Finally, we present an extensive case study of real-world complexity demonstrating that the proposed approach is applicable in realistic scenarios and can cope with different levels of resource efficiency.},
author = {Nikolas Roman Herbst and Samuel Kounev and Andreas Weber and Henning Groenda},
booktitle = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015)},
day = {18--19},
keywords = {IaaS, benchmark, metric, cloud, elasticity, resource, measurement},
location = {Firenze, Italy},
month = {May},
note = {Acceptance rate: 29\%},
pdf = {http://se2.informatik.uni-wuerzburg.de/pa/uploads/papers/paper-782.pdf},
slides = {http://se2.informatik.uni-wuerzburg.de/pa/uploads/slides/slides-paper-782.pdf},
title = {{BUNGEE: An Elasticity Benchmark for Self-Adaptive IaaS Cloud Environments}},
year = {2015},
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}
@inproceedings{HeHuKoAm2013-ICPE-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.}},
acmid = {2479899},
address = {New York, NY, USA},
author = {Nikolas Roman Herbst and Nikolaus Huber and Samuel Kounev and Erich Amrehn},
booktitle = {Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (ICPE 2013)},
day = {21--24},
doi = {10.1145/2479871.2479899},
isbn = {978-1-4503-1636-1},
keywords = {arrival rate, proactive resource provisioning, time series analysis, workload forecasting},
location = {Prague, Czech Republic},
month = {April},
numpages = {12},
pages = {187--198},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/HeHuKoAm2013-ICPE-WorkloadClassificationAndForecasting.pdf},
publisher = {ACM},
slides = {http://sdqweb.ipd.kit.edu/publications/pdfs/HeHuKoAm2013-ICPE-WorkloadClassificationAndForecasting_Slides.pdf},
title = {{Self-Adaptive Workload Classification and Forecasting for Proactive Resource Provisioning}},
url = {http://doi.acm.org/10.1145/2479871.2479899},
year = {2013}
}
@inproceedings{HeKoRe2013-ICAC-Elasticity,
abstract = {{Originating from the field of physics and economics, the term elasticity is nowadays heavily used in the context of cloud computing. In this context, elasticity is commonly understood as the ability of a system to automatically provision and de-provision computing resources on demand as workloads change. However, elasticity still lacks a precise definition as well as representative metrics coupled with a benchmarking methodology to enable comparability of systems. Existing definitions of elasticity are largely inconsistent and unspecific leading to confusion in the use of the term and its differentiation from related terms such as scalability and efficiency; the proposed measurement methodologies do not provide means to quantify elasticity without mixing it with efficiency or scalability aspects. In this short paper, we propose a precise definition of elasticity and analyze its core properties and requirements explicitly distinguishing from related terms such as scalability, efficiency, and agility. Furthermore, we present a set of appropriate elasticity metrics and sketch a new elasticity tailored benchmarking methodology addressing the special requirements on workload design and calibration.}},
author = {Nikolas Roman Herbst and Samuel Kounev and Ralf Reussner},
booktitle = {Proceedings of the 10th International Conference on Autonomic Computing (ICAC 2013)},
day = {24--28},
location = {San Jose, CA},
month = {June},
note = {Acceptance Rate (Short Paper): 36.9\%},
pdf = {http://sdqweb.ipd.kit.edu/publications/pdfs/HeKoRe2013-ICAC-Elasticity.pdf},
publisher = {USENIX},
slides = {http://sdqweb.ipd.kit.edu/publications/pdfs/HeKoRe2013-ICAC-Elasticity_Slides.pdf},
title = {{Elasticity in Cloud Computing: What it is, and What it is Not}},
titleaddon = {{(Short Paper)}},
url = {https://www.usenix.org/conference/icac13/elasticity-cloud-computing-what-it-and-what-it-not},
year = {2013}
}
@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}
}
@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}
}
@incollection{herbst2017metrics,
title = {Metrics and Benchmarks for Self-aware Computing Systems},
author = {Herbst, Nikolas and Becker, Steffen and Kounev, Samuel and Koziolek, Heiko and Maggio, Martina and Milenkoski, Aleksandar and Smirni, Evgenia},
booktitle = {Self-Aware Computing Systems},
pages = {437--464},
year = {2017},
publisher = {Springer International Publishing}
}