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Adaptive Solutions to Resource Provi...

Desmarais, Ronald J.

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Adaptive Solutions to Resource Provisioning and Task Allocation Problems for Cloud Computing.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Adaptive Solutions to Resource Provisioning and Task Allocation Problems for Cloud Computing./
Author:	Desmarais, Ronald J.
Description:	164 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NS28358
ISBN:	9780499283580

Adaptive Solutions to Resource Provisioning and Task Allocation Problems for Cloud Computing.
Desmarais, Ronald J.

Adaptive Solutions to Resource Provisioning and Task Allocation Problems for Cloud Computing. - 164 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--University of Victoria (Canada), 2014.

With the emergence of the cloud computing paradigm, we can now provide dynamic resource provisioning at unprecedented levels of scalability. This flexibility constitutes a rich environment for researchers to experiment with new designs. Such experimental novel designs can take advantage of adaptation, controllability, selfconfiguration, and scheduling techniques to provide improved resource utilization while achieving service level agreements. This dissertation uses control and scheduling theories to develop new designs to improve resource utilization and service level agreement satisfaction. We optimize resource provisioning using the Cutting Stock problem formulation and control theory within feedback frameworks. We introduce a model-based method of control to manipulate the scheduling problem's formulation model to achieve desired results. We also present a control based method using Kalman filters for admission control. Finally, we present two case studies--- the Yakkit media social application and the Rigi Cloud testbed for deploying virtual machine experiments. The results of our investigations demonstrate that our approaches and techniques can optimize resource utilization, decrease service level agreement violations, and provide scheduling guarantees.

ISBN: 9780499283580Subjects--Topical Terms:

626642
Computer Science.

Adaptive Solutions to Resource Provisioning and Task Allocation Problems for Cloud Computing.
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245 1 0 $a Adaptive Solutions to Resource Provisioning and Task Allocation Problems for Cloud Computing.
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500 $a Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
500 $a Adviser: Hausi A. Muller.
502 $a Thesis (Ph.D.)--University of Victoria (Canada), 2014.
520 $a With the emergence of the cloud computing paradigm, we can now provide dynamic resource provisioning at unprecedented levels of scalability. This flexibility constitutes a rich environment for researchers to experiment with new designs. Such experimental novel designs can take advantage of adaptation, controllability, selfconfiguration, and scheduling techniques to provide improved resource utilization while achieving service level agreements. This dissertation uses control and scheduling theories to develop new designs to improve resource utilization and service level agreement satisfaction. We optimize resource provisioning using the Cutting Stock problem formulation and control theory within feedback frameworks. We introduce a model-based method of control to manipulate the scheduling problem's formulation model to achieve desired results. We also present a control based method using Kalman filters for admission control. Finally, we present two case studies--- the Yakkit media social application and the Rigi Cloud testbed for deploying virtual machine experiments. The results of our investigations demonstrate that our approaches and techniques can optimize resource utilization, decrease service level agreement violations, and provide scheduling guarantees.
590 $a School code: 0244.
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650 4 $a Engineering, System Science. $3 1018128
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710 2 $a University of Victoria (Canada). $b bsheaff@uvic.ca. $3 2049972
773 0 $t Dissertation Abstracts International $g 75-07B(E).
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