Energy Efficient Cloud Computing: Techniques and Tools

Data centers hosting internet-scale services consume megawatts of power. Mainly for cost reasons but also to appease environmental concerns, data center operators are interested to reduce their use of energy. This thesis investigates if and how hardware virtualization helps to improve the energy e...

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Bibliographic Details
Main Author: Knauth, Thomas
Other Authors: Technische Universität Dresden, Fakultät Informatik
Format: Doctoral Thesis
Language:English
Published: Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden 2015
Subjects:
Online Access:http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-164391
http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-164391
http://www.qucosa.de/fileadmin/data/qucosa/documents/16439/thesis-preflight-pdfa-2b.pdf
Description
Summary:Data centers hosting internet-scale services consume megawatts of power. Mainly for cost reasons but also to appease environmental concerns, data center operators are interested to reduce their use of energy. This thesis investigates if and how hardware virtualization helps to improve the energy efficiency of modern cloud data centers. Our main motivation is to power off unused servers to save energy. The work encompasses three major parts: First, a simulation-driven analysis to quantify the benefits of known reservation times in infrastructure clouds. Virtual machines with similar expiration times are co-located to increase the probability to power down unused physical hosts. Second, we propose and prototyped a system to deliver truly on-demand cloud services. Idle virtual machines are suspended to free resources and as a first step to power off the physical server. Third, a novel block-level data synchronization tool enables fast and efficient state replication. Frequent state synchronization is necessary to prevent data unavailability: powering down a server disables access to the locally attached disks and any data stored on them. The techniques effectively reduce the overall number of required servers either through optimized scheduling or by suspending idle virtual machines. Fewer live servers translate into proportional energy savings, as the unused servers must no longer be powered.