東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Autonomic performance and power cont...

Lama, Palden.

Linked to FindBook

Google Book

Amazon

博客來

Autonomic performance and power control in virtualized datacenters.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Autonomic performance and power control in virtualized datacenters./
Author:	Lama, Palden.
Description:	227 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3563619
ISBN:	9781303120435

Autonomic performance and power control in virtualized datacenters.
Lama, Palden.

Autonomic performance and power control in virtualized datacenters. - 227 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Colorado Springs, 2013.

Virtualized datacenters, the platform for supporting Cloud computing, allow diverse applications to share the underlying server resources. Due to the highly dynamic nature of Internet workloads, increasing complexity of applications, and complex dynamics of shared infrastructure, datacenters face significant challenges in managing application performance while maintaining resource utilization efficiency and reducing power consumption costs. This thesis presents middleware approaches to autonomic performance and power control in virtualized datacenters. To this end, we designed self-adaptive resource management techniques based on queuing models, machine learning and feedback control theory.

ISBN: 9781303120435Subjects--Topical Terms:

626642
Computer Science.

Autonomic performance and power control in virtualized datacenters.
LDR:03610nam a2200337 4500 001 1960760
005 20140624210002.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303120435
035 $a (MiAaPQ)AAI3563619
035 $a AAI3563619
040 $a MiAaPQ $c MiAaPQ
100 1 $a Lama, Palden. $3 2096474
245 1 0 $a Autonomic performance and power control in virtualized datacenters.
300 $a 227 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Xiaobo Zhou.
502 $a Thesis (Ph.D.)--University of Colorado at Colorado Springs, 2013.
520 $a Virtualized datacenters, the platform for supporting Cloud computing, allow diverse applications to share the underlying server resources. Due to the highly dynamic nature of Internet workloads, increasing complexity of applications, and complex dynamics of shared infrastructure, datacenters face significant challenges in managing application performance while maintaining resource utilization efficiency and reducing power consumption costs. This thesis presents middleware approaches to autonomic performance and power control in virtualized datacenters. To this end, we designed self-adaptive resource management techniques based on queuing models, machine learning and feedback control theory.
520 $a Firstly, we designed an efficient server provisioning mechanism based on end-to-end resource allocation optimization for client perceived response time guarantee in a multi-tier server cluster. To guarantee an important percentile-based performance in the face of highly dynamic workloads, we developed a self-adaptive and model-independent neural fuzzy controller, which is capable of self-constructing and adapting its server allocation policies.
520 $a Secondly, we developed a non-invasive and energy-efficient mechanism for performance isolation of co-located applications on virtualized servers. Thirdly, we designed a system that provides coordinated power and performance control in a virtualized server cluster through a Fuzzy MIMO controller. We further developed a distributed and interference-aware control framework for large complex systems.
520 $a Furthermore, we developed a power-aware framework based on GPU virtualization for managing scientific workloads running in GPU clusters. It improves the system energy efficiency through dynamic consolidation and placement of GPU workloads.
520 $a Finally, we developed an automation tool for joint resource allocation and configuration of Hadoop MapReduce framework, for cost-efficient Big Data Processing in the Cloud. It addresses the significant challenge of provisioning ad-hoc jobs that have performance deadlines through a novel two-phase machine learning and optimization framework.
520 $a We implemented and evaluated the proposed techniques in a testbed of virtualized blade servers hosting multi-tier applications, SPEC CPU2006 benchmarks, and Hadoop microbenchmarks. For evaluating power management of GPU clusters, we used NVIDIA Tesla C1060 GPUs. This thesis provides novel resource management solutions that control the quality of service provided by virtualized resources, improve the energy efficiency of the underlying system, and reduce the burden of complex system management from human operators.
590 $a School code: 0892.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, System Science. $3 1018128
690 $a 0984
690 $a 0790
710 2 $a University of Colorado at Colorado Springs. $b College of Engineering and Applied Science -Computer Science. $3 2096475
773 0 $t Dissertation Abstracts International $g 74-09B(E).
790 $a 0892
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3563619