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Title page for ETD etd-07212015-144644


Type of Document Dissertation
Author Caglar, Faruk
Author's Email Address faruk.caglar@vanderbilt.edu
URN etd-07212015-144644
Title Dynamic Resource Management in Resource-overbooked Cloud Data Centers
Degree PhD
Department Computer Science
Advisory Committee
Advisor Name Title
Dr. Aniruddha S. Gokhale Committee Chair
Dr. Akos Ledeczi Committee Member
Dr. Christopher J. White Committee Member
Dr. Douglas Schmidt Committee Member
Dr. Gautam Biswas Committee Member
Keywords
  • scheduler optimization
  • virtual machine placement
  • artificial intelligience
  • data center
  • cloud computing
  • resource management
Date of Defense 2015-07-08
Availability unrestricted
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.

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