%0 Conference Paper %1 GrHeSpKo-Closer18-RecServDemEst %A Grohmann, Johannes %A Herbst, Nikolas %A Spinner, Simon %A Kounev, Samuel %B Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018) %D 2018 %I SciTePress %K Automated_model_learning GoogleResearchAward LibReDE Optimization PRISMA Prediction Resource_management Self-adaptive-systems Statistical_estimation_and_machine_learning descartes t_visionposition %P 473--480 %R 10.5220/0006761104730480 %T Using Machine Learning for Recommending Service Demand Estimation Approaches %U https://doi.org/10.5220/0006761104730480 %X Service demands are key parameters in service and performance modeling. Hence, a variety of different approaches to service demand estimation exist in the literature. However, given a specific scenario, it is not trivial to select the currently best approach, since deep expertise in statistical estimation techniques is required and the requirements and characteristics of the application scenario might change over time (e.g., by varying load patterns). To tackle this problem, we propose the use of machine learning techniques to automatically recommend the best suitable approach for the target scenario. The approach works in an online fashion and can incorporate new measurement data and changing characteristics on-the-fly. Preliminary results show that executing only the recommended estimation approach achieves 99.6% accuracy compared to executing all approaches available, while speeding up the estimation time by 57%. %@ 978-989-758-295-0

@inproceedings{GrHeSpKo-Closer18-RecServDemEst, abstract = {Service demands are key parameters in service and performance modeling. Hence, a variety of different approaches to service demand estimation exist in the literature. However, given a specific scenario, it is not trivial to select the currently best approach, since deep expertise in statistical estimation techniques is required and the requirements and characteristics of the application scenario might change over time (e.g., by varying load patterns). To tackle this problem, we propose the use of machine learning techniques to automatically recommend the best suitable approach for the target scenario. The approach works in an online fashion and can incorporate new measurement data and changing characteristics on-the-fly. Preliminary results show that executing only the recommended estimation approach achieves 99.6% accuracy compared to executing all approaches available, while speeding up the estimation time by 57%.}, added-at = {2020-04-06T11:20:04.000+0200}, author = {Grohmann, Johannes and Herbst, Nikolas and Spinner, Simon and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/21d497785246c4e64ce795c2b18869881/se-group}, booktitle = {Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018)}, doi = {10.5220/0006761104730480}, interhash = {4f627c1c54da9829061bdcbd59d0a43c}, intrahash = {1d497785246c4e64ce795c2b18869881}, isbn = {978-989-758-295-0}, keywords = {Automated_model_learning GoogleResearchAward LibReDE Optimization PRISMA Prediction Resource_management Self-adaptive-systems Statistical_estimation_and_machine_learning descartes t_visionposition}, month = {March}, organization = {INSTICC}, pages = {473--480}, publisher = {SciTePress}, timestamp = {2021-01-12T12:08:43.000+0100}, title = {Using Machine Learning for Recommending Service Demand Estimation Approaches}, url = {https://doi.org/10.5220/0006761104730480}, year = 2018 }