語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
到查詢結果
[ subject:"Computer Science." ]
切換:
標籤
|
MARC模式
|
ISBD
Qualitative performance analysis for...
~
Buneci, Emilia S.
Qualitative performance analysis for large-scale scientific workflows.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Qualitative performance analysis for large-scale scientific workflows.
作者:
Buneci, Emilia S.
面頁冊數:
188 p.
附註:
Adviser: Daniel A. Reed.
附註:
Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 4254.
Contained By:
Dissertation Abstracts International69-07B.
標題:
Computer Science.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3315377
ISBN:
9780549657385
Qualitative performance analysis for large-scale scientific workflows.
Buneci, Emilia S.
Qualitative performance analysis for large-scale scientific workflows.
- 188 p.
Adviser: Daniel A. Reed.
Thesis (Ph.D.)--Duke University, 2008.
Experiments with two scientific applications from meteorology and astronomy comparing signatures generated from instantaneous values of performance data versus those generated from temporal characteristics support the former hypothesis that temporal information is necessary to extract from performance time series data to be able to accurately interpret the behavior of these applications. Furthermore, temporal signatures incorporating variance and pattern information generated for these applications reveal signatures that have distinct characteristics during well-performing versus poor-performing executions. This leads to the framework's accurate classification of instances of similar behaviors, which represents supporting evidence for the latter hypothesis. The proposed framework's ability to generate a qualitative assessment of performance behavior for scientific applications using temporal information present in performance time series data represents a step towards simplifying and improving the quality of service for Grid applications.
ISBN: 9780549657385Subjects--Topical Terms:
212513
Computer Science.
Qualitative performance analysis for large-scale scientific workflows.
LDR
:04564nam _2200337 _450
001
206901
005
20090413130037.5
008
090730s2008 ||||||||||||||||| ||eng d
020
$a
9780549657385
035
$a
00372113
040
$a
UMI
$c
UMI
100
$a
Buneci, Emilia S.
$3
321838
245
1 0
$a
Qualitative performance analysis for large-scale scientific workflows.
300
$a
188 p.
500
$a
Adviser: Daniel A. Reed.
500
$a
Source: Dissertation Abstracts International, Volume: 69-07, Section: B, page: 4254.
502
$a
Thesis (Ph.D.)--Duke University, 2008.
520
$a
Experiments with two scientific applications from meteorology and astronomy comparing signatures generated from instantaneous values of performance data versus those generated from temporal characteristics support the former hypothesis that temporal information is necessary to extract from performance time series data to be able to accurately interpret the behavior of these applications. Furthermore, temporal signatures incorporating variance and pattern information generated for these applications reveal signatures that have distinct characteristics during well-performing versus poor-performing executions. This leads to the framework's accurate classification of instances of similar behaviors, which represents supporting evidence for the latter hypothesis. The proposed framework's ability to generate a qualitative assessment of performance behavior for scientific applications using temporal information present in performance time series data represents a step towards simplifying and improving the quality of service for Grid applications.
520
$a
Most Grid application users are primarily concerned with obtaining the result of the application as fast as possible, without worrying about the details involved in monitoring and understanding factors affecting application performance. In this work, we aim to provide the application users with a simple and intuitive performance evaluation mechanism during the execution time of their long-running Grid applications or workflows. Our performance evaluation mechanism provides a qualitative and periodic assessment of the application's behavior by informing the user whether the application's performance is expected or unexpected. Furthermore, it can help improve overall application performance by informing and guiding fault-tolerance services when the application exhibits persistent unexpected performance behaviors.
520
$a
This thesis addresses the hypotheses that in order to qualitatively assess application behavioral states in long-running scientific Grid applications: (1) it is necessary to extract temporal information in performance time series data, and that (2) it is sufficient to extract variance and pattern as specific examples of temporal information. Evidence supporting these hypotheses can lead to the ability to qualitatively assess the overall behavior of the application and, if needed, to offer a most likely diagnostic of the underlying problem.
520
$a
To test the stated hypotheses, we develop and evaluate a general qualitative performance analysis framework that incorporates (a) techniques from time series analysis and machine learning to extract and learn from data, structural and temporal features associated with application performance in order to reach a qualitative interpretation of the application's behavior, and (b) mechanisms and policies to reason over time and across the distributed resource space about the behavior of the application.
520
$a
Today, large-scale scientific applications are both data driven and distributed. To support the scale and inherent distribution of these applications, significant heterogeneous and geographically distributed resources are required over long periods of time to ensure adequate performance. Furthermore, the behavior of these applications depends on a large number of factors related to the application, the system software, the underlying hardware, and other running applications, as well as potential interactions among these factors.
590
$a
School code: 0066.
650
$a
Computer Science.
$3
212513
690
$a
0984
710
$a
Duke University.
$b
Computer Science.
$3
321835
773
0
$g
69-07B.
$t
Dissertation Abstracts International
790
$a
0066
790
1 0
$a
Chase, Jeffrey S.
$e
committee member
790
1 0
$a
Ellis, Carla S.
$e
committee member
790
1 0
$a
Freeh, Vincent
$e
committee member
790
1 0
$a
Reed, Daniel A.,
$e
advisor
791
$a
Ph.D.
792
$a
2008
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3315377
$z
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3315377
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000024332
電子館藏
1圖書
電子書
TH
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3315377
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入