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Authors: André Hardt ; Abdulrahman Nahhas ; Hendrik Müller and Klaus Turowski

Affiliation: Faculty of Computer Science, Otto von Guericke University, Magdeburg, Germany

Keyword(s): Commercial-off-the-Shelf Enterprise Applications, Deep Reinforcement Learning, Hybrid Cloud.

Abstract: Making the right placement decision for large IT landscapes of enterprise applications in hybrid cloud environments can be challenging. In this work, we concentrate on deriving the best placement combination for standard enterprise IT landscapes with the specific use case of SAP-based systems based on performance real-world metrics. The quality of the placement decision is evaluated on the basis of required capacities, costs, various functional and business requirements, and constraints. We approach the problem through the use of deep reinforcement learning (DRL) and present two possible environment designs that allow the DRL algorithm to solve the problem. In the first proposed design, the placement decision for all systems in the IT landscape is performed at the same time, while the second solves the problem sequentially by placing one system at a time. We evaluate the viability of both designs with three baseline DRL algorithms: DQN, PPO, and A2C. The algorithms were able to succe ssfully explore and solve the designed environments. We discuss the potential performance advantages of the first design over the second but also note its challenges of scalability and compatibility with various types of DRL algorithms. (More)

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Paper citation in several formats:
Hardt, A., Nahhas, A., Müller, H., Turowski and K. (2025). Deep Reinforcement Learning for Selecting the Optimal Hybrid Cloud Placement Combination of Standard Enterprise IT Applications. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8; ISSN 2184-4992, SciTePress, pages 434-443. DOI: 10.5220/0013210800003929

@conference{iceis25,
author={André Hardt and Abdulrahman Nahhas and Hendrik Müller and Klaus Turowski},
title={Deep Reinforcement Learning for Selecting the Optimal Hybrid Cloud Placement Combination of Standard Enterprise IT Applications},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={434-443},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013210800003929},
isbn={978-989-758-749-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Deep Reinforcement Learning for Selecting the Optimal Hybrid Cloud Placement Combination of Standard Enterprise IT Applications
SN - 978-989-758-749-8
IS - 2184-4992
AU - Hardt, A.
AU - Nahhas, A.
AU - Müller, H.
AU - Turowski, K.
PY - 2025
SP - 434
EP - 443
DO - 10.5220/0013210800003929
PB - SciTePress

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