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Authors: Johannes Grohmann ; Nikolas Herbst ; Simon Spinner and Samuel Kounev

Affiliation: Universität Würzburg, Germany

Keyword(s): Service Demand Estimation, Machine Learning, Approach Selection, Service Modeling.

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%.

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Paper citation in several formats:
Grohmann, J.; Herbst, N.; Spinner, S. and Kounev, S. (2018). Using Machine Learning for Recommending Service Demand Estimation Approaches - Position Paper. In Proceedings of the 8th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-295-0; ISSN 2184-5042, SciTePress, pages 473-480. DOI: 10.5220/0006761104730480

@conference{closer18,
author={Johannes Grohmann. and Nikolas Herbst. and Simon Spinner. and Samuel Kounev.},
title={Using Machine Learning for Recommending Service Demand Estimation Approaches - Position Paper},
booktitle={Proceedings of the 8th International Conference on Cloud Computing and Services Science - CLOSER},
year={2018},
pages={473-480},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006761104730480},
isbn={978-989-758-295-0},
issn={2184-5042},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Cloud Computing and Services Science - CLOSER
TI - Using Machine Learning for Recommending Service Demand Estimation Approaches - Position Paper
SN - 978-989-758-295-0
IS - 2184-5042
AU - Grohmann, J.
AU - Herbst, N.
AU - Spinner, S.
AU - Kounev, S.
PY - 2018
SP - 473
EP - 480
DO - 10.5220/0006761104730480
PB - SciTePress

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