Authors:
Mohamed Esam Elsaid
1
;
Hazem M. Abbas
2
and
Christoph Meinel
1
Affiliations:
1
Internet Technologien und Systeme, Hasso-Plattner Institut, Potsdam Uni., Potsdam, Germany
;
2
Dept. Computer and Systems Engineering, Ain Shams University, Cairo, Egypt
Keyword(s):
Timing, Cloud Computing, Virtual, Live Migration, VMware, vMotion, Modeling, Overhead, Cost, Datacenter, Prediction, Machine Learning.
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 pred
iction and datacenter network utilization prediction. Datacenter admins can be alerted with this optimal timing recommendation when a live migration request is issued.
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