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Extending Layered Queueing Network with Hybrid Sub Models Representing Exceptions and D Ecision Making Extending Layered Queueing Network with Hybrid Sub­ models Representing Exceptions and Decision Making by Pengfei Wu B. Eng., M.A. Sc. A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering Ottawa-Carleton Institute for Electrical and Computer Engineering (OCIECE) Department of Systems and Computer Engineering Carle ton University Ottawa, Ontario, Canada, K1S 5B6 May 2013 © Copyright 2013, Pengfei Wu Library and Archives Bibliotheque et Canada Archives Canada Published Heritage Direction du 1+1 Branch Patrimoine de I'edition 395 Wellington Street 395, rue Wellington Ottawa ON K1A0N4 Ottawa ON K1A 0N4 Canada Canada Your file Votre reference ISBN: 978-0-494-94539-1 Our file Notre reference ISBN: 978-0-494-94539-1 NOTICE: AVIS: The author has granted a non­ L'auteur a accorde une licence non exclusive exclusive license allowing Library and permettant a la Bibliotheque et Archives Archives Canada to reproduce, Canada de reproduire, publier, archiver, publish, archive, preserve, conserve, sauvegarder, conserver, transmettre au public communicate to the public by par telecommunication ou par I'lnternet, preter, telecommunication or on the Internet, distribuer et vendre des theses partout dans le loan, distrbute and sell theses monde, a des fins commerciales ou autres, sur worldwide, for commercial or non­ support microforme, papier, electronique et/ou commercial purposes, in microform, autres formats. paper, electronic and/or any other formats. The author retains copyright L'auteur conserve la propriete du droit d'auteur ownership and moral rights in this et des droits moraux qui protege cette these. Ni thesis. Neither the thesis nor la these ni des extraits substantiels de celle-ci substantial extracts from it may be ne doivent etre imprimes ou autrement printed or otherwise reproduced reproduits sans son autorisation. without the author's permission. In compliance with the Canadian Conformement a la loi canadienne sur la Privacy Act some supporting forms protection de la vie privee, quelques may have been removed from this formulaires secondaires ont ete enleves de thesis. cette these. While these forms may be included Bien que ces formulaires aient inclus dans in the document page count, their la pagination, il n'y aura aucun contenu removal does not represent any loss manquant. of content from the thesis. Canada Abstract Model elements involving decision-making present a challenge to performance analysis based on queueing formalisms, if the decisions are related to performance quantities, as in some kinds of exception decisions. In a queueing model the probability of the exception must be assumed in advance. In a state-based model the decision can be represented directly and its probability determined, however state-based models often suffer from state explosion issue which makes the model unsolvable. This thesis creates the Hybrid Performance Modeling Methodology (HPMM). This methodology extends Layered Queueing Network (LQN) by combining it with Generalized Stochastic Petri Nets (GSPN), so that it can tackle systems with decision making and particularly with exceptions. This methodology is developed through systematic behaviour partitioning and modeling formalism selection, consistent sub-model construction with several approximation techniques, and an open tool development with the iteration solution. The tool has integrated the LQN solver and the GSPN solver so far. This methodology is further generalized as Aspect-Oriented Performance Modeling (AOPM) to provide more scalable performance analysis. HPMM is demonstrated through modeling a system with a range of resource allocation exception handling cases and systems with short and long run connection cases using the network communication protocol TCP. Acknowledgements First and foremost, I would like to express my special appreciation to my supervisor, Dr. Murray C. Woodside, for your valuable experience and advice throughout my Ph. D journey. Your seasoned guidance, broad vision, and numerous encouragements have been integral to this thesis work. Deepest gratitudes are also due to my committee members Dr. Ahmed E. Hassan, Dr. Amiya Nayak, Dr. Dorina C. Petriu, Dr. Gregory R. Franks, Dr. Chung-Horng, Lung, Dr. Michel Barbeau, and Dr. Voicu Groza for the valuable comments and suggestions on my research. Many thanks go to the friends from the labs in school and the office in the company, including Dorin B. Petriu, Ming Huo, Jim Li, Jin Xu, Tao Zhen, and Xiuping Wu, et al, providing countless hours of discussion. I would like to thank the members of the RADS Lab for creating a wonderful working environment and for their support. The financial assistance from Carleton University is appreciated. Special thanks to my family. Words can not express how grateful I am. I have got the patience, understanding and love since I was born. I would like to thank to my beloved wife, Fang Yang, for your encourage and support, and to my beloved son, Austin Huayu Wu, for being a good boy cheering me up. tv Table of Contents ABSTRACT............................................................................................................ I ll ACKNOWLEDGEMENTS................................................................................IV TABLE OF CONTENTS.....................................................................................V LIST OF FIGURES..............................................................................................IX LIST OF TABLES...............................................................................................XV 1. INTRODUCTION..........................................................................................1 1.1 M o t iv a t io n ............................................................................................................................................... 1 1.2 P r o p o se d M e t h o d o l o g y .................................................................................................................3 1.3 So l u t io n s f o r H y br id M o d e l in g ............................................................................................... 6 1.4 Contributions ........................................................................................................................................ 8 2. BACKGROUND.............................................................................................. 9 2.1 E x c e p t io n H a n d l in g M e c h a n is m s ............................................................................................9 2.1.1 Exception Handling Definition ........................................................................................9 2.1.2 Exception Handling Models ...........................................................................................11 2.1.3 Behaviour Analysisfor Exception Handling ....................................................................13 2.2 Sc e n a r io M o d e l N o t a t io n s ........................................................................................................14 2.3 P er fo r m a n c e M o d e l in g F o r m a lism s .....................................................................................18 2.3.1 Queueing Network Modeling ..........................................................................................19 2.3.2 State-Based Modeling ....................................................................................................21 2.3.3 Comparison among various methodologies ........................................................................ 22 2.4 Sel e c t e d P er fo r m a n c e M o d e l F o rm alism N o t a t io n s .............................................25 2.5 T ransformation fro m Sc e n a r io M o d e l t o P e r f o r m a n c e M o d e l .....................29 3. CHALLENGES AND THE APPROACH TO A SOLUTION 31 3.1 T h e P r o b l e m ...........................................................................................................................................31 3.2 T h e St a t e o f t h e A r t .......................................................................................................................33 3.3 W e a k n e ss o f t h e St a t e o f t h e Ar t ........................................................................................36 3.4 H y b r id Pe r fo r m a n c e M o d e l in g M e t h o d o l o g y O v e r v ie w ..................................38 3.5 H y br id P e r fo r m a n c e M o d e l in g P r o c e d u r e ...................................................................42 4. HYBRID PERFORMANCE MODELING METHODOLOGY....44 4.1 Sc o pe o f H y br id P e r f o r m a n c e M o d e l in g M e t h o d o l o g y .................................... 44 4.2 B e h a v io u r P a r t it io n w it h M o d e l F orm alism Se l e c t io n .......................................47 4.3 Su b -M o d e l Construction ............................................................................................................ 51 4.3.1 Behaviour Sub-Scenario Construction from Behaviour Cells ................................................51 4.3.2 Behaviour Sub-Scenario Construction of an 'Example System ............................................ 52 4.3.3 Performance Sub-Model Construction from Behaviour Sub-Scenario ...................................55 4.3.4 Interaction among Performance Sub-Models.
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