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Automatic ARIMA time series modeling...

Tran, Nancy Ngoc.

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Automatic ARIMA time series modeling and forecasting for adaptive input/output prefetching.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automatic ARIMA time series modeling and forecasting for adaptive input/output prefetching./
Author:	Tran, Nancy Ngoc.
Description:	162 p.
Notes:	Adviser: Daniel A. Reed.
Contained By:	Dissertation Abstracts International63-02B
Subject:	Computer Science -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3044246
ISBN:	0493581219

Automatic ARIMA time series modeling and forecasting for adaptive input/output prefetching.
Tran, Nancy Ngoc.

Automatic ARIMA time series modeling and forecasting for adaptive input/output prefetching. - 162 p.

Adviser: Daniel A. Reed.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.

This thesis presents a comprehensive software framework—Automodeler—to provide automatic modeling and forecasting for input/output (I/O) request interarrival times. In Automodeler, ARIMA models of interarrival times are automatically identified and built during application execution. Model parameters are recursively estimated in real-time for every new request arrival, adapting to changes that are intrinsic or external to the running application. Online forecasts are subsequently generated based on the updated parameters.

ISBN: 0493581219Subjects--Topical Terms:

890869
Computer Science

Automatic ARIMA time series modeling and forecasting for adaptive input/output prefetching.
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020 $a 0493581219
035 $a (UnM)AAI3044246
035 $a AAI3044246
040 $a UnM $c UnM
100 1 $a Tran, Nancy Ngoc. $3 1260350
245 1 0 $a Automatic ARIMA time series modeling and forecasting for adaptive input/output prefetching.
300 $a 162 p.
500 $a Adviser: Daniel A. Reed.
500 $a Source: Dissertation Abstracts International, Volume: 63-02, Section: B, page: 0883.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002.
520 $a This thesis presents a comprehensive software framework—Automodeler—to provide automatic modeling and forecasting for input/output (I/O) request interarrival times. In Automodeler, ARIMA models of interarrival times are automatically identified and built during application execution. Model parameters are recursively estimated in real-time for every new request arrival, adapting to changes that are intrinsic or external to the running application. Online forecasts are subsequently generated based on the updated parameters.
520 $a Our goal is to improve application-level I/O performance by using forecasts to guide the prefetch of I/O requests before they arrive, reducing stall time. We combine a just-in-time prefetcher with ARIMA time series predictions and Markov model spatial predictions [41] to adaptively determine when, what, and how many data blocks to prefetch.
520 $a To validate our approach, we built a prototype that integrates adaptive prefetching with caching and local disk striping in the PPFS2 [51] testbed. Results obtained for a computational physics code demonstrate 30% improvement in total execution time over the traditional Unix file system on three Linux clusters, equipped with different hardware configurations. More importantly, this performance improvement has small memory requirements and is shown to scale with increasing I/O intensity
590 $a School code: 0090
650 $a Computer Science $3 890869
690 $a 098
710 2 $a University of Illinois at Urbana-Champaign $3 1260348
773 0 $t Dissertation Abstracts International $g 63-02B
790 $a 009
790 1 $a Reed, Daniel A., $e adviso
791 $a Ph.D
792 $a 200
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3044246