Live Migration Timing Optimization for VMware Environments using Machine Learning Techniques

Mohamed Elsaid, Hazem Abbas, Christoph Meinel

2020

Abstract

Live migation of Virtual Machines (VMs) is a vital feature in virtual datacenters and cloud computing platforms. Pre-copy live migration techniques is the commonly used technique in virtual datacenters hypervisors including VMware, Xen, Hyper-V and KVM. This is due to the robustness of pre-copy technique compared to post-copy or hybrid-copy techniques. The disadvantage of pre-copy live migration type is the challenge to predict the live migration cost and performance. So, virtual datanceters admins run live migration without an idea about the expected cost and the optimal timing for running live migration especially for large VMs or for multiple VMs running concurrently. This leads to longer live migration duration, network bottlenecks and live migration failure in some cases. In this paper, we use machine learning techniques to predict the optimal timing for running a live migration request. This optimal timing approach is based on using machine learning for live migration cost prediction and datacenter network utilization prediction. Datacenter admins can be alerted with this optimal timing recommendation when a live migration request is issued.

Paper Citation

in Harvard Style

Elsaid M., Abbas H. and Meinel C. (2020). Live Migration Timing Optimization for VMware Environments using Machine Learning Techniques.In Proceedings of the 10th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-424-4, pages 91-102. DOI: 10.5220/0009397300910102

in Bibtex Style

@conference{closer20,
author={Mohamed Elsaid and Hazem Abbas and Christoph Meinel},
title={Live Migration Timing Optimization for VMware Environments using Machine Learning Techniques},
booktitle={Proceedings of the 10th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2020},
pages={91-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009397300910102},
isbn={978-989-758-424-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Live Migration Timing Optimization for VMware Environments using Machine Learning Techniques
SN - 978-989-758-424-4
AU - Elsaid M.
AU - Abbas H.
AU - Meinel C.
PY - 2020
SP - 91
EP - 102
DO - 10.5220/0009397300910102