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Title
Virtual machine migration energy consumption simulation in cloud computing / by Vincenzo De Maio
AuthorDe Maio, Vincenzo
Thesis advisorProdan, Radu
PublishedInnsbruck, 2016
Descriptionxiv, 136 Seiten : Illustrationen
Institutional NoteUniversität Innsbruck, Dissertation, 2016
Date of SubmissionJuly 2016
LanguageEnglish
Document typeDissertation (PhD)
Keywords (DE)energy consumption / cloud computing / distributed computing / machine learning / linear regression
URNurn:nbn:at:at-ubi:1-5074 Persistent Identifier (URN)
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 The work is publicly available
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Abstract (German)

Energy consumption has become a significant issue for data centres. For this reason, many researchers currently focus on developing energy aware algorithms to improve their energy efficiency. However, due to the difficulty of employing real data centres' infrastructure for assessing the effectiveness of energy-aware algorithms, researchers resort on simulation tools. These tools require precise and detailed models for virtualized data centres in order to deliver accurate results. In recent years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine (VM) migration or do not consider some of the energy impacting components (e.g. CPU, network, storage). In this work, I focus on increasing the accuracy of existing energy prediction models, by providing the research community with a more accurate data centre energy consumption simulator. To this end, I focus on designing an accurate model of energy consumption of VM migration.

First, I present a comparative analysis of the energy consumption of the software stack of two of today's mostly used network interface cards (NICs) in data centres, Ethernet and Infiniband. I carefully design for this purpose a set of benchmark experiments to assess the impact of different traffic patterns and interface settings on energy consumption. Using these benchmarking results, I derive an energy consumption model for network transfers and evaluate its accuracy for a VM migration scenario. I also propose guidelines for NIC selection from an energy efficiency perspective for different application classes.

Then, I show that omitting VM migration and workload variation from the models could lead to inaccurate consumption estimates. For this reason, I propose a new model for data centre energy consumption that takes into account the previously omitted model parameters and provides accurate energy consumption predictions for paravirtualised VMs running on homogeneous hosts. The new model's accuracy is evaluated with a comprehensive set of operational scenarios. With the use of these scenarios I present a comparative analysis of my model with similar state-of-the-art models for energy consumption of VM migration, showing an improvement up to $24$\% in accuracy of prediction.

Finally, I propose a new model for data centre energy consumption that takes into account the previously omitted components and provides more accurate energy consumption predictions compared to other state-of-the-art solutions for paravirtualized VMs. I evaluate this model's accuracy in a comprehensive set of scenarios implemented in the \mbox

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