dc.creator | Caglar, Faruk | |
dc.date.accessioned | 2020-08-22T17:44:58Z | |
dc.date.available | 2015-07-21 | |
dc.date.issued | 2015-07-21 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-07212015-144644 | |
dc.identifier.uri | http://hdl.handle.net/1803/13388 | |
dc.description.abstract | The rapid growth of social media, mobile data traffic, and
sensors that surround us are giving rise to very large volumes of
data, which must then be processed in a timely and scalable manner to
make informed decisions. The elastic properties of the cloud makes
it suitable to address these data processing challenges. Despite this
promise, however, numerous challenges remain unresolved, which pertain
to operating a cloud data center in a way that lends itself to energy
conservation, and provides effective resource management which
improves resource utilization while satisfying application performance
requirements, and security. This doctoral research makes the
following four contributions to address a subset of these challenges.
First, it presents a dynamic and adaptive algorithm to reconfigure the
parameters of the hypervisor scheduler that effectively schedules the
virtual machines (VMs) on a host in response to anticipated workload
changes. Second, it provides a model-predictive algorithm that
balances the need to utilize resources effectively by promoting
maximal overbooking while still honoring the soft real-time
requirements of applications. Third, it provides novel solutions for
VM placement that accounts for VM performance interference. Fourth,
it presents an effective runtime virtual machine placement technique
that identifies an aptly suited host machine to host a VM that is to
be migrated by considering both power and performance. The doctoral
research has utilized real-world traces of cloud data centers to
develop and validate the solutions.
The long lasting impact of this dissertation stems from that fact that
each solution provides a systematic and scientific approach that a
cloud service provider can implement in their data centers to address
energy consumption and resource utilization challenges. | |
dc.format.mimetype | application/pdf | |
dc.subject | scheduler optimization | |
dc.subject | virtual machine placement | |
dc.subject | artificial intelligience | |
dc.subject | data center | |
dc.subject | cloud computing | |
dc.subject | resource management | |
dc.title | Dynamic Resource Management in Resource-overbooked Cloud Data Centers | |
dc.type | dissertation | |
dc.contributor.committeeMember | Dr. Christopher J. White | |
dc.contributor.committeeMember | Dr. Douglas Schmidt | |
dc.contributor.committeeMember | Dr. Gautam Biswas | |
dc.contributor.committeeMember | Dr. Akos Ledeczi | |
dc.type.material | text | |
thesis.degree.name | PHD | |
thesis.degree.level | dissertation | |
thesis.degree.discipline | Computer Science | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2015-07-21 | |
local.embargo.lift | 2015-07-21 | |
dc.contributor.committeeChair | Dr. Aniruddha S. Gokhale | |