Towards Predicting Resource Demands and Performance of Distributed Cloud Services
S. Dräxler, M. Peuster, M. Illian, H. Karl, Towards Predicting Resource Demands and Performance of Distributed Cloud Services, Wien, Darmstadt, 2018.
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Understanding the behavior of distributed cloud service components in different load situations is important for efficient and automatic management and orchestration of these services. For this purpose and for practical research in distributed cloud computing in general, there is need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of application components and the expected performance of applica- tions. We present initial results for predicting the interdependence between resource demands and performance characteristics using support vector regression and polynomial regression models. The data gathered from our experiments is publicly available.
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KuVS-Fachgespräch Fog Computing 2018
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Dräxler S, Peuster M, Illian M, Karl H. Towards Predicting Resource Demands and Performance of Distributed Cloud Services. Wien, Darmstadt; 2018.
Dräxler, S., Peuster, M., Illian, M., & Karl, H. (2018). Towards Predicting Resource Demands and Performance of Distributed Cloud Services. Wien, Darmstadt.
@book{Dräxler_Peuster_Illian_Karl_2018, place={Wien, Darmstadt}, series={KuVS-Fachgespräch Fog Computing 2018}, title={Towards Predicting Resource Demands and Performance of Distributed Cloud Services}, author={Dräxler, Sevil and Peuster, Manuel and Illian, Marvin and Karl, Holger}, year={2018}, collection={KuVS-Fachgespräch Fog Computing 2018} }
Dräxler, Sevil, Manuel Peuster, Marvin Illian, and Holger Karl. Towards Predicting Resource Demands and Performance of Distributed Cloud Services. KuVS-Fachgespräch Fog Computing 2018. Wien, Darmstadt, 2018.
S. Dräxler, M. Peuster, M. Illian, and H. Karl, Towards Predicting Resource Demands and Performance of Distributed Cloud Services. Wien, Darmstadt, 2018.
Dräxler, Sevil, et al. Towards Predicting Resource Demands and Performance of Distributed Cloud Services. 2018.
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