Data Driven Meta-Heuristic-Assisted Approach for Placement of Standard IT Enterprise Systems in Hybrid-Cloud

Andrey Kharitonov, Abdulrahman Nahhas, Hendrik Müller, Klaus Turowski

2023

Abstract

We address the problem of hybrid-cloud placement selection for commercial off-the-shelf IT enterprise applications with the sizing done based on workload profiles collected from real-world production systems. The proposed approach leverages techniques based on evolutionary meta-heuristics with a multi-criteria weighted sum objective function. A placement decision is made between an on-premises data center and a public cloud, using real pricing information for virtual machines, storage, and networking published by the public cloud vendor via automation APIs and on-premises cost estimation as a share of expense per service. Additional objectives, such as expertise and non-functional requirements, are encoded in a numerical form for the objective function. The evaluation is performed as single and multi-objective optimization by employing genetic algorithm, and non-dominated-sorting genetic-algorithm-III on the case study of an SAP landscape hybrid-cloud placement on a selected public cloud with real workload data collected during day-to-day business operations, indicating the viability of the approach.

Paper Citation

in Harvard Style

Kharitonov A., Nahhas A., Müller H. and Turowski K. (2023). Data Driven Meta-Heuristic-Assisted Approach for Placement of Standard IT Enterprise Systems in Hybrid-Cloud. In Proceedings of the 13th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-650-7, SciTePress, pages 139-146. DOI: 10.5220/0011726600003488

in Bibtex Style

@conference{closer23,
author={Andrey Kharitonov and Abdulrahman Nahhas and Hendrik Müller and Klaus Turowski},
title={Data Driven Meta-Heuristic-Assisted Approach for Placement of Standard IT Enterprise Systems in Hybrid-Cloud},
booktitle={Proceedings of the 13th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2023},
pages={139-146},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011726600003488},
isbn={978-989-758-650-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Data Driven Meta-Heuristic-Assisted Approach for Placement of Standard IT Enterprise Systems in Hybrid-Cloud
SN - 978-989-758-650-7
AU - Kharitonov A.
AU - Nahhas A.
AU - Müller H.
AU - Turowski K.
PY - 2023
SP - 139
EP - 146
DO - 10.5220/0011726600003488
PB - SciTePress