@inproceedings{5efaf77a4c6d4b74804a2af608eb9a31,
title = "Performance and energy-based cost prediction of virtual machines live migration in clouds",
abstract = "Virtual Machines (VMs) live migration is one of the important approaches to improve resource utilisation and support energy efficiency in Clouds. However, VMs live migration leads to performance loss and additional costs due to increased migration time and energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs live migration by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the workload, power consumption and total cost for heterogeneous VMs before and after live migration, with the possibility of recovering the migration cost e.g. 28.48\% for the predicted cost recovery of the VM.",
keywords = "Cloud computing, Cost prediction, Live migration, Power consumption, Workload prediction",
author = "Moahammad Aldosaary and Karim Djemame",
note = "Publisher Copyright: Copyright {\textcopyright} 2018 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.; 8th International Conference on Cloud Computing and Services Science, CLOSER 2018 ; Conference date: 19-03-2018 Through 21-03-2018",
year = "2018",
doi = "10.5220/0006682803840391",
language = "English",
series = "CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science",
publisher = "SciTePress",
pages = "384--391",
editor = "Munoz, \{Victor Mendez\} and Donald Ferguson and Markus Helfert and Claus Pahl",
booktitle = "CLOSER 2018 - Proceedings of the 8th International Conference on Cloud Computing and Services Science",
}
