Virtual Machine Boot Time Model

Thuy Nguyen 1 Adrien Lebre 1, 2
1 ASCOLA - Aspect and Composition Languages
Inria Rennes – Bretagne Atlantique , LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : —Cloud computing by far brings a lot of undeniable advantages. Accordingly, many research works aim to evaluate the characteristics of cloud systems on many aspects such as performance, workload, cost, provisioning policies, and resources management. In order to setup a cloud system for running rigorous experiments, ones have to overcome a huge amount of challenges and obstacles to build, deploy, and manage systems and applications. Cloud simulation tools help researchers to focus only on the parts they are interested about without facing the aforementioned challenges. However, cloud simulators still do not provide accurate models for Virtual Machine (VM) operations. This leads to incorrect results in evaluating real cloud systems. Following previous works on live-migration, we present in this paper an experimental study we conducted in order to propose a first-class VM boot time model. Most cloud simulators often ignore the VM boot time or give a naive model to represent it. After studying the relationship between the VM boot time and different system parameters such as CPU utilization, memory usage, I/O and network bandwidth, we introduce a first boot time model that could be integrated into current cloud simulators. Through experiments, we also confirmed that our model correctly reproduced the boot time of a VM under different resources contention.
Type de document :
Communication dans un congrès
PDP 2017 - 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing, Mar 2017, St Peterbourg, Russia. pp.430 - 437, 2017, 〈10.1109/PDP.2017.58〉
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Soumis le : mercredi 13 septembre 2017 - 14:20:52
Dernière modification le : mercredi 29 novembre 2017 - 09:33:45

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Thuy Nguyen, Adrien Lebre. Virtual Machine Boot Time Model. PDP 2017 - 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing, Mar 2017, St Peterbourg, Russia. pp.430 - 437, 2017, 〈10.1109/PDP.2017.58〉. 〈hal-01586932〉

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