A Study of Virtual Machine Placement Optimization in Data Centers

Stephanie Challita, Fawaz Paraiso, Philippe Merle

Abstract

In recent years, cloud computing has shown a valuable way for accommodating and providing services over the Internet such that data centers rely increasingly on this platform to host a large amount of applications (web hosting, e-commerce, social networking, etc.). Thus, the utilization of servers in most data centers can be improved by adding virtualization and selecting the most suitable host for each Virtual Machine (VM). The problem of VM placement is an optimization problem aiming for multiple goals. It can be covered through various approaches. Each approach aims to simultaneously reduce power consumption, maximize resource utilization and avoid traffic congestion. The main goal of this literature survey is to provide a better understanding of existing approaches and algorithms that ensure better VM placement in the context of cloud computing and to identify future directions.

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Paper Citation


in Harvard Style

Challita S., Paraiso F. and Merle P. (2017). A Study of Virtual Machine Placement Optimization in Data Centers . In Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-243-1, pages 343-350. DOI: 10.5220/0006236503430350


in Bibtex Style

@conference{closer17,
author={Stephanie Challita and Fawaz Paraiso and Philippe Merle},
title={A Study of Virtual Machine Placement Optimization in Data Centers},
booktitle={Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2017},
pages={343-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006236503430350},
isbn={978-989-758-243-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - A Study of Virtual Machine Placement Optimization in Data Centers
SN - 978-989-758-243-1
AU - Challita S.
AU - Paraiso F.
AU - Merle P.
PY - 2017
SP - 343
EP - 350
DO - 10.5220/0006236503430350