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Capacity Management in Hyper-Scale Datacenters using Predictive Modelling
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 80 credits / 120 HE creditsStudent thesis
Abstract [en]

Big data applications have become increasingly popular with the emerge of cloud computing and the explosion of artificial intelligence. Hence, the increasing adoption of data-hungry machines and services is driving the need for more power to keep the datacenters of the world running. It has become crucial for large IT companies such as Google, Facebook, Amazon etc. to monitor the energy efficiency of their datacenters’ facilities and take actions on optimization of these heavy consumers of electricity. This master thesis work proposes several predictive models to forecast PUE (Power Usage Effectiveness), regarded as the industry-de-facto metric for measuring datacenter’s IT power efficiency. This approach is a novel capacity management technique to predict and monitor the environment in order to prevent future disastrous events, which are strictly unacceptable in datacenter’s business.

Place, publisher, year, edition, pages
2019. , p. 51
Keywords [en]
Capacity Management, Efficient Datacentre, Predictive Modelling. Accuracy Metrics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-72487OAI: oai:DiVA.org:ltu-72487DiVA, id: diva2:1276314
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level (120 credits)
Supervisors
Available from: 2019-01-10 Created: 2019-01-08 Last updated: 2019-01-10Bibliographically approved

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fulltext(2660 kB)47 downloads
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Ruci, Xhesika
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf