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 cloud resource management.

Tunc, Cihan.

Linked to FindBook

Google Book

Amazon

博客來

Autonomic cloud resource management.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Autonomic cloud resource management./
Author:	Tunc, Cihan.
Description:	133 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
Contained By:	Dissertation Abstracts International76-07B(E).
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3682339
ISBN:	9781321556582

Autonomic cloud resource management.
Tunc, Cihan.

Autonomic cloud resource management. - 133 p.

Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.

Thesis (Ph.D.)--The University of Arizona, 2015.

This item is not available from ProQuest Dissertations & Theses.

The power consumption of data centers and cloud systems has increased almost three times between 2007 and 2012. The traditional resource allocation methods are typically designed for high performance as the primary objective to support peak resource requirements. However, it is shown that server utilization is between 12% and 18%, while the power consumption is close to those at peak loads. Hence, there is a pressing need for devising sophisticated resource management approaches. State of the art dynamic resource management schemes typically rely on only a single resource such as core number, core speed, memory, disk, and network. There is a lack of fundamental research on methods addressing dynamic management of multiple resources and properties with the objective of allocating just enough resources for each workload to meet quality of service requirements while optimizing for power consumption. The main focus of this dissertation is to simultaneously manage power and performance for large cloud systems. The objective of this research is to develop a framework of performance and power management and investigate a general methodology for an integrated autonomic cloud management.

ISBN: 9781321556582Subjects--Topical Terms:

621879
Computer engineering.

Autonomic cloud resource management.
LDR:03331nmm a2200301 4500 001 2072425
005 20160808080351.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781321556582
035 $a (MiAaPQ)AAI3682339
035 $a AAI3682339
040 $a MiAaPQ $c MiAaPQ
100 1 $a Tunc, Cihan. $3 1674819
245 1 0 $a Autonomic cloud resource management.
300 $a 133 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
500 $a Advisers: Salim Hariri; Ali Akoglu.
502 $a Thesis (Ph.D.)--The University of Arizona, 2015.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a The power consumption of data centers and cloud systems has increased almost three times between 2007 and 2012. The traditional resource allocation methods are typically designed for high performance as the primary objective to support peak resource requirements. However, it is shown that server utilization is between 12% and 18%, while the power consumption is close to those at peak loads. Hence, there is a pressing need for devising sophisticated resource management approaches. State of the art dynamic resource management schemes typically rely on only a single resource such as core number, core speed, memory, disk, and network. There is a lack of fundamental research on methods addressing dynamic management of multiple resources and properties with the objective of allocating just enough resources for each workload to meet quality of service requirements while optimizing for power consumption. The main focus of this dissertation is to simultaneously manage power and performance for large cloud systems. The objective of this research is to develop a framework of performance and power management and investigate a general methodology for an integrated autonomic cloud management.
520 $a In this dissertation, we developed an autonomic management framework based on a novel data structure, AppFlow, used for modeling current and near-term future cloud application behavior. We have developed the following capabilities for the performance and power management of the cloud computing systems: 1) online modeling and characterizing the cloud application behavior and resource requirements; 2) predicting the application behavior to proactively optimize its operations at runtime; 3) a holistic optimization methodology for performance and power using number of cores, CPU frequency, and memory amount; and 4) an autonomic cloud management to support the dynamic change in VM configurations at runtime to simultaneously optimize multiple objectives including performance, power, availability, etc.
520 $a We validated our approach using RUBiS benchmark (emulating eBay), on an IBM HS22 blade server. Our experimental results showed that our approach can lead to a significant reduction in power consumption upto 87% when compared to the static resource allocation strategy, 72% when compared to adaptive frequency scaling strategy, and 66% when compared to a multi-resource management strategy.
590 $a School code: 0009.
650 4 $a Computer engineering. $3 621879
690 $a 0464
710 2 $a The University of Arizona. $b Electrical and Computer Engineering. $3 2094163
773 0 $t Dissertation Abstracts International $g 76-07B(E).
790 $a 0009
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3682339