Cost-based partitioning for distributed simulation of hierarchical modular DEVS models

Persistent Link:
http://hdl.handle.net/10150/280322
Title:
Cost-based partitioning for distributed simulation of hierarchical modular DEVS models
Author:
Park, Sunwoo
Issue Date:
2003
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
The main objective of this research is to design and implement a class of generic partitioning algorithms for hierarchical, modular Discrete Event Specification System (DEVS) models for distributed simulation. To attain the goal of this dissertation, a set of partitioning algorithms is designed using the cost analysis methodology. For more than a decade, abundant research has been conducted to develop partitioning algorithms that can find optimal, or reasonably acceptable, solutions for various partitioning problems. These employ methods such as simulated annealing, random partitioning, heuristic partitioning, and hierarchical clustering. In this dissertation, a new Generic Model Partitioning (GMP) algorithm for hierarchical modular DEVS models is proposed for distributed simulation. The GMP algorithm decomposes a given hierarchical model into a set of partition blocks and provides reasonable solutions for distinct partitioning problems based on a cost analysis methodology. It also minimizes model decomposition during the partitioning process and guarantees incremental quality of partitioning (QoP) improvements until a best partitioning is attained. A series of cost measures for cost generation, cost evaluation, and cost aggregation are introduced. Since a cost measure is a parametric method, subject to certain axioms, the proposed algorithm is generic and applicable any family of models provided there is a way to manipulate the appropriate cost information. A class of advanced algorithms derived from the GMP algorithm is also presented to tackle sophisticated issues associated with various distributed system configurations.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Computer Science.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Electrical and Computer Engineering
Degree Grantor:
University of Arizona
Advisor:
Zeigler, Bernard P.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleCost-based partitioning for distributed simulation of hierarchical modular DEVS modelsen_US
dc.creatorPark, Sunwooen_US
dc.contributor.authorPark, Sunwooen_US
dc.date.issued2003en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractThe main objective of this research is to design and implement a class of generic partitioning algorithms for hierarchical, modular Discrete Event Specification System (DEVS) models for distributed simulation. To attain the goal of this dissertation, a set of partitioning algorithms is designed using the cost analysis methodology. For more than a decade, abundant research has been conducted to develop partitioning algorithms that can find optimal, or reasonably acceptable, solutions for various partitioning problems. These employ methods such as simulated annealing, random partitioning, heuristic partitioning, and hierarchical clustering. In this dissertation, a new Generic Model Partitioning (GMP) algorithm for hierarchical modular DEVS models is proposed for distributed simulation. The GMP algorithm decomposes a given hierarchical model into a set of partition blocks and provides reasonable solutions for distinct partitioning problems based on a cost analysis methodology. It also minimizes model decomposition during the partitioning process and guarantees incremental quality of partitioning (QoP) improvements until a best partitioning is attained. A series of cost measures for cost generation, cost evaluation, and cost aggregation are introduced. Since a cost measure is a parametric method, subject to certain axioms, the proposed algorithm is generic and applicable any family of models provided there is a way to manipulate the appropriate cost information. A class of advanced algorithms derived from the GMP algorithm is also presented to tackle sophisticated issues associated with various distributed system configurations.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectComputer Science.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorZeigler, Bernard P.en_US
dc.identifier.proquest3090011en_US
dc.identifier.bibrecord.b44425776en_US
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