@inproceedings{472418a233e349bb920e170314beb41d,
title = "Virtual machine consolidation for cloud data centers using parameter-based adaptive allocation",
abstract = "Cloud computing enables cloud providers to offer computing infrastructure as a service (IaaS) in the form of virtual machines (VMs). Cloud management platforms automate the allocation of VMs to physical machines (PMs). An adaptive VM allocation policy is required to handle changes in the cloud environment and utilize the PMs efficiently. In the literature, adaptive VM allocation is typically performed using either reservation-based or demand-based allocation. In this work, we have developed a parameter-based VM consolidation solution that aims to mitigate the issues with the reservation-based and demand-based solutions. This parameter- based VM consolidation exploits the range between demand-based and reservation-based finding VM to PM allocations that strike a delicate balance according to cloud providers' goals. Experiments conducted using CloudSim show how the proposed parameter- based solution gives a cloud provider the flexibility to manage the trade-off between utilization and other requirements.",
keywords = "cloud data centers, efficient data center utilization, virtual machine consolidation, virtual machine mapping, parameter-based adaptive allocation, virtual machines (VMs), CloudSim, data centers",
author = "Abdelkhalik Mosa and Rizos Sakellariou",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 5th European Conference on the Engineering of Computer-Based Systems, ECBS 2017 ; Conference date: 31-08-2017 Through 01-09-2017",
year = "2017",
month = aug,
day = "31",
doi = "10.1145/3123779.3123807",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "1--10",
editor = "Ondrej Rysavy and Valentino Vranic",
booktitle = "Proceedings - 5th European Conference on the Engineering of Computer-Based Systems, ECBS 2017",
address = "United States",
}