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Exploring design configurations of s...

University of Illinois at Urbana-Champaign.

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Exploring design configurations of system models: From simultaneous simulation to search heuristics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring design configurations of system models: From simultaneous simulation to search heuristics./
Author:	Gaonkar, Shravan.
Description:	162 p.
Notes:	Adviser: William H. Sanders.
Contained By:	Dissertation Abstracts International70-02B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3347386
ISBN:	9781109025354

Exploring design configurations of system models: From simultaneous simulation to search heuristics.
Gaonkar, Shravan.

Exploring design configurations of system models: From simultaneous simulation to search heuristics. - 162 p.

Adviser: William H. Sanders.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.

In essence, this dissertation "develops algorithms and techniques that would enable an efficient methodology to compare large numbers of alternative configurations in order to speedup the design evaluation and validation process".

ISBN: 9781109025354Subjects--Topical Terms:

626642
Computer Science.

Exploring design configurations of system models: From simultaneous simulation to search heuristics.
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020 $a 9781109025354
035 $a (UMI)AAI3347386
035 $a AAI3347386
040 $a UMI $c UMI
100 1 $a Gaonkar, Shravan. $3 1043595
245 1 0 $a Exploring design configurations of system models: From simultaneous simulation to search heuristics.
300 $a 162 p.
500 $a Adviser: William H. Sanders.
500 $a Source: Dissertation Abstracts International, Volume: 70-02, Section: B, page: 1120.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.
520 $a In essence, this dissertation "develops algorithms and techniques that would enable an efficient methodology to compare large numbers of alternative configurations in order to speedup the design evaluation and validation process".
520 $a Simulation is applied in numerous and diverse fields, such as manufacturing systems, communications and protocol design, financial and economic engineering, operations research, design of transportation networks and systems, and so forth. The real utility of simulation lies in the ability to compare and evaluate alternative designs before actual implementation or deployment of a system. To perform a thorough analysis of a large number of configurations with varying system design parameter values, it is important to develop efficient simulation and design space exploration methods that can evaluate a large number of alternative system configurations quickly and accurately.
520 $a In situations where it is practical to exhaustively explore design parameter space, we proposed a new approach, called Simultaneous Simulation of Alternative System Configurations (SSASC), to evaluate dependability models that combines adaptive uniformization in simulation with the SCMS technique. SSASC showed that a significant speed-up can be achieved compared to traditional discrete-event simulation to evaluate all alternative configurations. The event set management using adaptive clock algorithm and efficient data structures to manage system model's state access and update enables efficient simulation. Using SSASC, design engineers can benefit from quicker evaluation of their system designs with better accuracy (due to variance reduction) than traditional simulation approaches provide.
520 $a In situations where complete design exploration is not practical, this dissertation provides an intelligent search space exploration technique to efficiently determine near optimal solutions. This dissertation provides a technique, called design solver (DS), to determine near-optimal designs using meta-search heuristics. DS achieves efficiency in exploring the design parameter space by first determining the parameter values that have major impact on the quality of the design solution, and then determining parameter values that further fine-tunes the quality of the design solution. That decomposition reduces the size of the search space, allowing IDS algorithm to focus on the most relevant regions to achieve a near-optimal solution.
590 $a School code: 0090.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, System Science. $3 1018128
690 $a 0790
690 $a 0984
710 2 $a University of Illinois at Urbana-Champaign. $3 626646
773 0 $t Dissertation Abstracts International $g 70-02B.
790 $a 0090
790 1 0 $a Sanders, William H., $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3347386