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`Chapman & Hall/CRC
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`Chapman & Hall/ CRC
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`CONTEXT-AWARE
`COMPUTING AND
`SELF-MANAGING
`SYSTEMS
`
`Edited by
`Waltenegus Dargie
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`0 CRC Press
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`Library of Congress Cataloging-in-Publication Data
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`Context-aware computing and self-managing systems / [edited by] Waltenegus
`Dargie. -- 1st ed.
`p. cm. -- (Context-aware computing and self-managing systems)
`Includes bibliographical references and index.
`ISBN 978-1-4200-7771-1 (alk. paper)
`1. Autonomic computing. I. Dargie, Waltenegus.
`
`QA76.9.A97C65 2009
`004--dc22
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`Page 6 of 202
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`

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`To Pheben, with love. Welcome to the world.
`
`V
`
`Page 7 of 202
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`

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`Page 8 of 202
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`Page 8 of 202
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`

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`Preface
`
`This book brings two research issues together: context-aware computing and
`the self-managing aspect of autonomous computing. Context-aware com(cid:173)
`puting is an extensively researched area, while self-managing systems are
`emerging. The goal of this book is to investigate the various roles context(cid:173)
`aware computing can play to develop self-managing systems, where a self(cid:173)
`management system can be a device, a middleware, an application, or a net(cid:173)
`work.
`The first chapter of the book identifies aspects that are common to both
`context-aware computing and autonomous computing. It offers a basic def(cid:173)
`inition of context-awareness and provides several examples ~ more focus is
`given to the acquisition, presentation and management of context informa(cid:173)
`tion. It presents as well basic aspects of self-managing systems and offers a
`few examples of self-managing systems.
`The remaining part of the book is divided into context-awareness and self(cid:173)
`management. The context-awareness subpart demonstrates how a context
`can be employed to make systems smart; how a context can be captured
`and represented; and how dynamic binding of context sources can be pos(cid:173)
`sible. The self-management subpart of the book demonstrates the need for
`"implicit-knowledge" to develop fault-tolerant and self-protective systems. It
`also presents a higher-level vision of future large-scale networks.
`Several researchers have participated in editing this book. I would like to
`acknowledge the contributions of Prof. Noriaki Kuwahara (Kyoto Institute
`of Technology), Prof. Ren Ohmura (Keio University), Prof. Markus Endler
`( Catholic University of Rio de Janeiro), Prof. Antonio Alfredo F. Loureiro
`(The Federal University of Minas Gerais), Prof. Mieso Denko (University
`of Guelph), and Dr. Daniel Schuster (Technical University of Dresden) for
`reviewing some of the chapters. Of course, there were also a plethora of
`reviewers whose names I have not mentioned here, but who have reviewed
`each chapter of the book and provided critical feedbacks.
`I would _like to acknowledge the contribution of my former post graduate
`student, Rami Mochaourab, who worked tirelessly with LaTeX to provide the
`book the shape it now has. He was always available, always willing to try
`new ideas, and always on time. Without his support, the book would never
`be finished on time.
`
`Dr. Waltenegus Dargie
`Technical University of Dresden
`Germany
`
`vii
`
`Page 9 of 202
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`

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`Page 10 of 202
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`Page 10 of 202
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`

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`Contributors
`
`Abe, Akinori
`ATR Knowledge Science
`Laboratories
`Kyoto, Japan
`
`Catalan, Marisa
`Wireless Networks Group
`Department of Telematics
`Technical University of Catalonia
`Barcelona, Spain
`
`Beltran, Victoria
`Wireless Networks Group
`Department of Telematics
`Technical University of Catalonia
`Barcelona, · Spain
`
`Charif, Yasmine
`Laboratoire d'Informatique de Paris
`Universite Paris
`Paris, France
`
`Breitman, Karin
`Departamento de Informatica
`Pontificia Universidade Cat6lica
`(PUC-RJ)
`Rio de Janeiro, Brazil
`
`Dargie, Waltenegus
`Chair of Computer Networks
`Faculty of Computer Science
`Technical University of Dresden
`Dresden, Germany
`
`Briot, Jean-Pierre
`Departamento de Informatica
`Pontificia Universidade Cat6lica
`(PUC-RJ)
`Rio de Janeiro, Brazil
`
`Davoli, Franco
`Department of Communications,
`Computer, and Systems Science
`University of Genoa
`Genoa Italy
`
`Bouvry, Pascal
`University of Luxemburg
`Luxemburg
`
`Ding, Jianguo
`University of Luxembourg
`Luxembourg
`
`Casademont, Jordi
`Wireless Networks Group
`Department of Telematics
`Technical University of Catalonia
`Barcelona, Spain
`
`El Fallah Seghrouchni, Amal
`Laboratoire d'Informatique de Paris
`U niversite Paris
`Paris, France
`
`ix
`
`Page 11 of 202
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`

`

`X
`
`Endler, Markus
`Departamento de Informatica
`Pontificia U niversidade Catolica
`(PUC-RJ)
`Rio de Janeiro, Brazil
`
`Guan, Haibing
`School of Information Security
`Engineering
`Shanghai Jiao Tong University
`Shanghai, P. R. China
`
`Hadjiantonis, Antonis M
`Centre for Communication Systems
`Research
`Department of Electronic
`Engineering,
`University of Surrey
`Guildford, UK
`
`Hartel, Pieter
`University of Twente
`Enschede, The Netherlands
`
`Klauser, Bruno
`Cisco Europe
`Glattzentrum, Switzerland
`
`Kogure, Kiyoshi
`ATR Knowledge Science
`Laboratories
`Kyoto, Japan
`
`Kramer, Bernd J.
`Fern U niversitat in Hagen
`Hagen, Germany
`
`Kuwahara, Noriaki
`Kyoto Institute of Technology
`Kyoto, Japan
`
`Liang, Alei
`Software School
`Shanghai Jiao Tong University
`Shanghai, P. R. China
`
`Liu, Lei
`Sun Microsystems, Inc.
`Menlo Park, CA, USA
`
`Luis Ferrer, Jose
`Wireless Networks Group
`Department of Telematics
`Technical University of Catalonia
`Barcelona, Spain
`
`Mazuel, Laurent
`Laboratoire d'Informatique de Paris
`Universite Paris
`Paris, France
`
`N aya, Futoshi
`ATR Knowledge Science
`Laboratories
`Kyoto, Japan
`
`Ohboshi, Naoki
`Kinki University
`Osaka, Japan
`
`Page 12 of 202
`
`

`

`Ozaku, Hiromi Itoh
`ATR Knowledge Science
`Laboratories
`Kyoto, Japan
`
`xi
`
`Sanchez-Loro, Xavier
`Wireless Networks Group
`Department of Telematics
`Technical University of Catalonia
`Barcelona, Spain
`
`Paradells, Josep
`Wireless Networks Group
`Department of Telematics
`Technical University of Catalonia
`Barcelona, Spain
`
`Pavlou, George
`Centre for Communication Systems
`Research
`Department of Electronic
`Engineering
`University of Surrey
`Guildford, UK
`
`Scholten, Hans
`University of Twente
`Enschede, The Netherlands
`
`Sundramoorthy, Vasughi
`Lancaster University
`Lancaster, UK
`
`Viterbo, Jose
`Departamento de Informatica
`Pontificia U niversidade Catolica
`(PUC-RJ)
`Rio de Janeiro, Brazil
`
`Sabouret, Nicolas
`Laboratoire d'Informatique de Paris,
`Universite Paris
`Paris, France
`
`Wolter, Ralf
`Cisco Systems
`Duesseldorf, Germany
`
`Page 13 of 202
`
`

`

`Page 14 of 202
`
`Page 14 of 202
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`

`

`Contents
`
`1 Context and Self-Management
`W altenegus Dargie
`Introduction .
`1.1
`1.2 Aspects of Self-Management .
`1.3 Examples of Self-Managing Systems
`1.3.1 Self-Managing Chaotic Networks
`1.3.2 Recovery-Oriented Computing
`1.4 Context-Aware Computing
`1.4.1 Context-Awareness .
`1.4.2 Surrounding Context .
`1.4.3 Activity on a Street
`1.4.4 User's Attention in a Meeting .
`1.4.5 Activity Context from Multiple Sensors
`!Badge.
`1.4.6
`1.4.7 Mediacup
`1.5 Context-Aware, Self-Managing Systems
`1.6 Organization of the Book
`Refere.nces .
`;
`2 Verifying Nursing Activities Based on Workflow Model
`Noriaki Kuwahara, Naoki Ohboshi, Hiromi Itoh Ozaku, Futoshi Naya,
`Akinori Abe, and Kiyoshi Kogure
`Introduction . . . . . . . . .
`2.1
`. . . . . . .
`2.2 Related Works
`2.3 Overview of Research Goals
`2.4 Case Study of Intravenous Medication Process Performed by
`Nurses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`2.4.1 Survey Method and Results . . . . . . . . . . . . . . .
`2.4.2 Possible Solutions from Ubiquitous Computing Point of
`View. . . . . . . . . . . . . . . . . . . . .
`2.5 Prototype of Ubiquitous Sensor Network System
`. . . . .
`2.5.1 Experimental Room Description
`2.5.2 Location Tracking by IR-ID . . . . . . . .
`2.5.3 Activity Data Collection with Bluetooth-Based Wire-
`less Accelerometers . . . . . . . . . . . . . . .
`2.5.4 Feature Extraction for Activity Recognition .
`2.6 Algorithm for Detecting Errors in Nursing Activities
`
`1
`
`1
`2
`3
`3
`4
`5
`5
`7
`8
`9
`10
`10
`11
`11
`12
`12
`
`15
`
`16
`17
`19
`
`21
`22
`
`23
`26
`26
`27
`
`27
`29
`29
`
`xiii
`
`Page 15 of 202
`
`

`

`xiv
`
`2.6.1 Nursing Workflow Model
`2.6.2 Error Detection Algorithm
`2.7 Testing Our Proposed Algorithm
`2. 7 .1 Data Correction Method for Recording History of N urs-
`ing Activities . . . . .
`2.7.2 Test Results . . . . . .
`2.8 Conclusion and Future Works
`References . . . . . . . . . . . . . .
`
`3 A Taxonomy of Service Discovery Systems
`Vasughi Sundramoorthy, Pieter Hartel, and Hans Scholten
`3.1
`Introduction . . . . . . . . . . . . . . . . . . . . . . . .
`3.2 Service Discovery: Third Generation Name Discovery
`3.3 Service Discovery Architecture
`. . .
`3.3.1 Logical Topologies (Overlays)
`3.3.2 Non-Registry Topologies . .
`3.3.3 Registry-Based Topologies . .
`3.4 Service Discovery Functions . . . . .
`3.5 Operational Aspects of Service Discovery
`3.6 State of the Art . . . .
`3.6.1 Small Systems
`. . . .
`3.6.2 Large Systems
`. . . .
`3. 7 Taxonomy of State of the Art
`3.7.1 Taxonomy of State of the Art Solutions to Operational
`Aspects
`. . . . . . . . . . . . . . . . . . . . . . . . . .
`3.7.2 Taxonomy of Service Discovery Functions and Methods
`3.8 Conclusion
`References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`4 Managing Distributed and Heterogeneous Context for Am-
`bient Intelligence
`Jose Viterbo, Markus Endler, Karin Breitman and Laurent Mazuel,
`Yasmine Charif, Nicolas Sabouret, Amal El Fallah Seghrouchni, and
`Jean-Pierre Briot
`4.1
`Introduction . .
`4.1.1 Scenario
`4.1.2 Outline
`4.2 Fundamental Concepts .
`4.2.1 Ambient Intelligence
`4.2.2 Context Awareness .
`4.2.3 Ontology
`. . . . . .
`4.2.4 Context Reasoning .
`4.3 Ontological Representation and Reasoning about Context
`4.3.1 Evaluation Criteria and Taxonomy
`4.3.2 Gaia . .
`4.3.3 CoBrA . . . . . . . . . . . . . . . .
`
`30
`31
`34
`
`35
`35
`37
`38
`
`43
`
`44
`47
`49
`50
`50
`51
`54
`57
`59
`61
`64
`66
`
`66
`68
`72
`73
`
`79
`
`79
`81
`83
`83
`83
`83
`84
`85
`86
`87
`88
`90
`
`I
`
`Page 16 of 202
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`

`

`4.3.4 Semantic Space .
`4.3.5 CHIL
`4.3.6 SAMOA.
`4.3.7 CAMUS.
`4.3.8 OWL-SF
`4.3.9 DRAGO.
`4.3.10 Conclusion
`4.4 Approaches for Ontology Alignment
`4.4.1 Lexics1J Alignment
`4.4.2 Structural Approaches .
`Instances-Based Approaches .
`4.4.3
`4.4.4 Mediated Approaches
`4.4.5 Alignment Based on Semantic Similarity .
`4.4.6 Conclusion
`4.5 The Campus Approach .
`4.5.1 Context Types
`4.5.2 Ontologies .
`4.5.3 Reasoning .
`4.5.4 Ontology Alignment
`4.6 Conclusion and Open Problems
`4.6.1 Discussion and Future Work.
`References .
`5 Dynamic Content ·Negotiation in Web Environments
`Xavier Sanchez-Laro, Jordi Casademont, Jose Luis Ferrer, Victoria
`Beltran, Marisa Catalan and Iosep Paradells
`Introduction .
`5.1
`5.2 Ubiquitous Web .
`5.2.1 Related Concepts .
`5.2.2 Protocols Overview .
`5.3 A Proxy-Based Solution for the Detection of Device Capabili-
`ties .
`5.3.1 System Description .
`5.3.2 System Deployment
`5.3.3 Vocabulary
`5.4 Collaborative Optimization, Context Acquisition and Provi-
`. .
`sioning .
`5.4.1 Application Layer Optimization .
`5.4.2 System Description .
`5.4.3 Header Restoring Policies and Context Provisioning
`5.4.4 Collaborative Device Capabilities Detection Service
`5.4.5 Optimization Results .
`5.5 Conclusion
`References .
`
`xv
`
`92
`93
`95
`97
`99
`101
`102
`104
`105
`107
`107
`108
`109
`111
`111
`112
`113
`113
`116
`120
`120
`122
`
`129
`
`130
`131
`133
`135
`
`142
`143
`151
`152
`
`154
`155
`157
`160
`165
`168
`170
`171
`
`Page 17 of 202
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`

`

`xvi
`
`177
`
`6 The Road towards Self-Management in Communication Net-
`works
`Ralf Wolter and Bruno Klauser
`6.1
`Introduction . . . . . . . . . . . . . . . . . .
`177
`6.2 Self-Management in Networks . . . . . . . .
`179
`6.3 Defining Concrete Steps towards the Vision
`184
`6.3.1 Define Business Objectives in a Business Language
`184
`6.3.2 Translate the Business Objectives into Technical Terms 185
`6.3.3 Derive Rules and Policies for Systems
`. . . . . . . . .
`188
`6.3.4 Automatically Breakdown Goals
`. . . . . . . . . . . .
`190
`6.3.5 Enable Network Elements to Interpret, Deploy, and Com-
`ply with These Goals .
`6.4 Research Outlook .
`References . . . . . . . . . . . . . .
`
`193
`197
`199
`
`7
`
`7.4
`
`202
`202
`205
`208
`208
`214
`
`Policy-Based Self-Management in Wireless Networks
`Antonis M. Hadjiantonis and George Pavlou
`7.1
`Introduction, Background and State-of-the-Art
`7.1.1 Self-Management Concepts and Challenges
`..
`7.1.2 Open Issues and Motivation .
`7.2 Policies and Context for Self-Management
`7.2.1 Policy-Based Management (PBM) Principles
`7.2.2 Context and Context-Awareness
`7.2.3 Management of Wireless Ad Hoc Networks and Self-
`Management Capabilities
`219
`7.3 A Framework for the Self-Management of Wireless Networks
`223
`7.3.1 High Level Framework Overview and Design
`224
`7.3.2 Policy-Based and Context-Aware Organizational Model 225
`7.3.3 Policy-Based Design for Autonomic Decision Making .
`229
`7.3.4 Context-Aware Platform for Information Collection and
`Modeling
`7.3.5 Distributed Policy and Context Repositories - The Im-
`portance of Knowledge Management
`7.3.6 Context and Policies Interaction for Closed-Loop Auto-
`..
`nomic Management
`7.3.7 Overview of Applicability and Policy Examples
`Implementation and Evaluation of Self-Management Capabili-
`ties .
`7.4.1 Self-Configuration and Self-Optimization in Wireless Ad
`Hoc Networks .
`..
`7.4.2 Self-Configuration of a Distributed Policy Repository .
`7.4.3 Self-Protection of User Privacy and Preferences
`7.5 Conclusions and the Future of Self-management .
`7.5.1 Summary and Concluding Remarks
`7.5.2 Future Trends and Challenges .
`
`201
`
`233
`
`236
`
`238
`239
`
`243
`
`244
`253
`256
`258
`258
`260
`
`Page 18 of 202
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`

`

`xvii
`
`262
`262
`264
`
`273
`
`7.6 Acknowledgments.
`7.7 Abbreviations .
`References . . . . . . . .
`8 Autonomous Machine Learning Networks
`Lei Liu
`Introduction . . . . . . . . . . . . .
`8.1
`8.2 Problem Formulation . . . . . . . .
`8.2.1 Attack Prediction Problem
`8.2.2 Attack Class Discovery Problem
`8.3 Related Work
`8.4 Methodology
`8.5 Evaluation . .
`8.6 Experiment
`8.6.1 Data Samples .
`8.6.2 Sample Reduction
`Initial Arbitrary Network
`8.6.3
`8.6.4 Tuning . . . . . . . . . . .
`8.6.5 Comparison of Class Prediction .
`8.6.6 Comparison of Cluster Prediction .
`8.7 Conclusions
`References . . . . . . . . . . . . . . . . . . . . .
`309
`9 Probabilistic Fault Management
`Jianguo Ding, Pascal Bouvry, Bernd J. Kramer, Haibing Guan, Alei
`Liang, and Franco Davoli
`309
`Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`9.1
`312
`. . . . . . . . .
`9.2 Probabilistic Inference in Fault Management
`9.2.l The Characteristics of the Faults in Distributed Systems 312
`315
`9.2.2 Bayesian Networks for Fault Management . . . . . . .
`9.2.3 Probabilistic Inference for Distributed Fault Manage-
`ment. . . . . . . . . . . . . . . . . . . . . . . . . . . .
`9.3 Prediction Strategies for Fault Management in Dynamic Net-
`320
`. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`works
`320
`9.3.1 Dynamic Characteristics in Networks.. . . . . . . . .
`322
`9.3.2 Dynamic Bayesian Networks for Fault Management
`322
`9.3.3 Prediction Strategies for Network Management . . .
`9.4 Application Investigations for Probabilistic Fault Management 325
`325
`9.4.1 Architecture for Network Management . . . . . . . . .
`328
`9.4.2 The Structure and Function of Fault Diagnosis Agent
`340
`9.4.3 Discussion of Application Issues.
`342
`9.5 Conclusions
`342
`References .
`
`274
`277
`279
`280
`282
`286
`288
`291
`291
`292
`295
`295
`295
`301
`301
`302
`
`318
`
`Index
`
`349
`
`Page 19 of 202
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`

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`Page 20 of 202
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`Page 20 of 202
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`

`

`List of Tables
`
`2.1 Top five categories of collected cases of nurses' awareness
`..
`2.2 Nurse's contexts and their features
`2.3 Test results verifying nursing activities based on nursing work-
`flow model.
`
`3.1 Service discovery functionalities
`
`4.1 Benefits of adopting formal ontology to model ambient knowl-
`edge in Campus.
`4.2 Classification of middleware systems for context-oriented onto-
`logical reasoning.
`4.3 A short example of the code for ontology alignment.
`
`5.1 Detection processes supported by type of device.
`. . . . . .
`5.2 Size in bytes of the Google search engine.
`5.3 Size· in bytes of detection-related transactions over each net-
`work section.
`
`24
`25
`
`36
`
`56
`
`85
`
`103
`119
`
`147
`163
`
`166
`
`6.1 Summary of existing self-management solutions building blocks 195
`222
`247
`250
`252
`255
`
`7.1 Taxonomy of related work on MANET management
`7.2 Wireless ad hoc networks self-management policies
`7.3 Wireless testbed specifications .
`Initial channel assignment measurements .
`7.4
`7.5 DPR management policies .
`
`9.1 The structure of event database.
`9.2 The JPD obtaining between every pair of parent nodes (X) and
`their son node (Y).
`
`336
`
`338
`
`xix
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`List of Figures
`
`2.1 Overview of E-Nightingale Project's target systems
`2.2 Modular structure of proposed systems . . . . . . .
`. . . . .
`"Nursing Resource" and rule descriptions
`2.3
`2.4 Scheduled IV drip process and possible errors in each step
`2.5 Experimental room layout . . . . . . . . . . . . . . . . . .
`2.6 Location labeling from numbers of received IR-IDs at each re-
`ceiver within a 1-second window in time line . . . . . . . . . .
`2.7 Subject wearing four Bluetooth-based triaxial accelerometers
`2.8 Nursing workflow model . . . . . . . . . . . . . . . . . .
`2.9 Confirming detected errors in observed nursing activities
`2.10 Event-driven voice recording set .
`2.11 Nurse A's observed data . . . . . . . . .
`
`3.1 Registry and non-Registry architectures
`3.2 Logical non-Registry topologies. . . . . .
`. . . . . . .
`3.3 Logical Registry topologies.
`3.4 Summary of operational design aspects and solutions.
`3.5 Taxonomy of state-of-the-art solutions to operational aspects.
`Shaded service discovery systems support the proposed solu(cid:173)
`tions. Appl means the solution to the operational aspect is
`supported by the application layer. Some systems depend on
`solutions provided by the undeJ:'lying protocol stacks, such as
`TCP, IP, Bluetooth and ad-hoc routing protocols.
`3.6 Taxonomy of state-of-the-art functional implementation. . . .
`
`4.1 Ontology alignment is a set of equivalences between nodes of
`both ontologies. This schema presents the three classical so(cid:173)
`lutions: alignment based on the structural properties, align-
`ment based on the concepts instances and alignment based on
`. . . . . . . . . . . . . . . . . . . .
`a "background ontology."
`4.2 Structural alignment error example based on hierarchy analy-
`sis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`Instance-based alignment allows construction of subsumption
`alignment in addition to equivalence alignment.
`4.4 Mediated alignment approach. . . . . .
`4.5 Abstract view of Campus architecture. . . . . .
`
`4.3
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`4.6 Campus multi-agent architecture ...
`4. 7 CATO ontology alignment strategy.
`
`5.1 Elements involved in Web delivery.
`5.2 HTTP request example.
`. .
`5.3 CC/PP profile example. . . . . . .
`5.4 CC/PPex request example.
`. . . .
`5.5 Device capabilities detection process.
`5.6 Proxy Web navigation. . . . . . .
`5.7 Proxy deployment configurations.
`5.8 System architecture.
`. . . . . . .
`5.9 Deployment diagram. . . . . . . .
`5.10 External detection use case sequence diagram. .
`5.11 Local detection use case sequence diagram.
`. .
`5.12 Average size of HTTP requests in uplink [bytes].
`5.13 Average improvement in response time (%)
`.
`
`6.1 Flow of starting a diesel engine 50 years ago .
`6.2 Relationship between service provider and service consumer
`6.3 Service decomposition
`. . . . . . . . . . . . . . . . . .
`6.4 TMF key indicator hierarchy
`. . . . . . . . . . . . . .
`6.5 Manage the communication infrastructure intuitively .
`6.6 Top-down approach for business objectives . .
`6. 7 Virtualization layer for network management
`6.8 Cisco embedded event manager . . . . . . .
`6.9 Self-optimizing with Cisco IP SLA
`. . . . .
`6.10 Self-protection through user authentication
`
`7.4
`
`7.1 Closed-loop controller
`. . . . . . . . . . . .
`7.2 Functional diagram of IBM's autonomic manager (K-MAPE)
`7.3 Mapping of proposed high-level framework to autonomic man-
`ager component . . . . . . . . . . . . . . . . . . . . . . . . . .
`IETF's framework for PBM (a) block diagram, (b) generic
`UML notation . . . . . . . . . . . . . . . . . . . . . . . . . . .
`7.5 Taxonomy of context information . . . . . . . . . . . . . . . .
`7.6 General diagram of closed-loop management with context and
`policies
`. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`7.7 Organizational models: (a) hybrid, (b) hierarchical,
`( c) distributed
`. . . . . . . . . . . . . . . . . . . . . . . . . .
`7.8 Block diagram of each role and internal components
`. . . . .
`7.9 Hybrid organizational model with internal components and in(cid:173)
`formation flow
`. . . . . . . . . . . . . . . . . . . . . . . . . .
`7.10 Policy-based and context-aware components and interactions
`7.11 Traditional (left) and proposed (right) policy repository deploy-
`ment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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`7.12 Diagram of closed-loop adaptation at different levels
`.
`7.13 Replication degrees depending on network fluidity
`. . . .
`7.14 Wireless ad hoc network testbed deployment
`7.15 Packet measurements at node Z for same channel deployment
`. . . . . .
`(1,1) and for consecutive channel deployment (2,1)
`7.16 Policy-based channel assignment measurements . . . . . . . .
`7.17 Testbed measurements of goodput using dynamic channel switch.
`Top: Moving average, Bottom: Instantaneous
`. . .
`7.18 Policy free and policy conforming objects
`
`8.1 Dynamic machine learning detection algorithm
`8.2 Machine tuning algorithm . . . . . . . . . . . .
`Initialization of all parameters . . . . . . . . . .
`8.3
`8.4 Randomized algorithm to locate action with maximum state-
`action value mapping . . . . . . . . . . . . . .
`8.5 Action simulation algorithm . . . . . . . . . .
`8.6 Update state-action value mapping algorithm
`. . . . . . . . . . . .
`8. 7 Locate policy algorithm
`8.8 KDDCup99 attack classes . . . . . . . . . . .
`8.9 Classification prediction result for Q learning
`8.10 MP prediction result for Q learning.
`8.11 Autonomous tuning scenario
`
`9.1 A model of fault propagation.
`9.2 Model of dynamic Bayesian network.
`9.3 Detailed model of network management with FDA.
`9.4 The structure of fault diagnosis agent.
`9.5 The procedure of event management.
`. . . .
`9.6 Dependency analysis in events.
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`Chapter 1
`
`Context and Self-Management
`
`Waltenegus Dargie
`Technical University of Dresden, 01062, Dresden, Germany
`
`. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`Introduction
`1.1
`1.2 Aspects of Self-Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`. . .. . .. . . .. . . .. . .. .. .. .. . . . .. . .. . . ..
`1.3 Examples of Self-Managing Systems
`1.4 Context-Aware Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`1.5 Context-Aware, Self-Managing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`1.6 Organization of the Book .. . . .. .. . .. .. . .. . .. .. .. .. .. . .. .. .. . .. . .. .. .. .. ..
`. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`References
`
`1
`2
`3
`5
`11
`12
`12
`
`Abstract
`This chapter provides an introduction to Context-Aware Computing and
`Self-Managing Systems. It begins by explaining why self-management is de(cid:173)
`sirable in complex systems and by describing self-management aspects (self(cid:173)
`configuration, self-optimization, self-healing and self-protection). For all these
`features, a self-managing system's needs to have a perpetual awareness of what
`is taking place both within itself and without. It is this duly awareness of one's
`state and surrounding that leads to self-adaptation. As a result, the chapter
`tries to demonstrate the scope and usefulness of context-aware computing in
`developing self-managing systems.
`
`Introduction
`1. 1
`Computing systems are becoming very complex, highly heterogeneous and
`distributed. At the same time, the users of these systems are usually mo(cid:173)
`bile and demand greater flexibility and efficiency in terms of response time,
`resource utilization, robustness, etc., to achieve critical business goals. The
`implication is that operating and maintaining computing systems is becoming
`an increasingly expensive business. In fact, Fox and Patterson claim that an(cid:173)
`nual outlays for maintenance, repair and operations far excerd total hardware
`and software costs, for both individuals and corporations [l].
`This high cost of ownership of computing systems has resulted in a number
`
`1
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`2
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`Context-Aware Computing and Self-Managing Systems
`
`of industry initiatives to reduce the burden of operations and management
`by making computing systems - at least gradually - self-managing. A few
`examples are IBM's Autonomic Computing, HP's Adaptive Infrastructure
`and Microsoft's Dynamic System Initiatives [2].
`Self-management derives its basic principles from the autonomous nervous
`system, which governs our heart rate and body temperature, thus freeing our
`conscious brain from the burden of dealing with these and many other low(cid:173)
`level, yet vital, functions [3]. This essential principle, if transferred well, en(cid:173)
`ables computing systems, whether acting individually or collectively, to receive
`higher-level objectives from their operators (users) but manage to maintain
`and adjust their operation in the face of changing components, workloads,
`demands and external conditions as well as imminent hardware and software
`failures.
`According to Kephart and Chess [3] and Tesauro et al. [4], a self-managing
`system contains an autonomic manager software and a (hardware or software)
`managed element. The managed element is what is being made self-managing
`and provides a sensing and actuating interface. Through the sensing interface,
`an array of sensors measure vital internal as well as external (environmental)
`phenomena which may potentially influence the system's short and long term
`performance. The actuating interface provides a way for the autonomic man(cid:173)
`ager to modify the behavior of the managed element. The autonomic manager
`itself contains components for monitoring and analyzing sensor data and for
`planning and executing management policies. Common to all of these compo(cid:173)
`nents is knowledge of the computing environment and service-level agreement
`as well as other related facts.
`The monitoring component inside the autonomic manager is responsible for
`reducing the amount of raw sensor data by applying filtering and correlation
`operations on the data. The analysis component gets refined data from the
`monitoring component in order to identify emerging or foreseeable problems
`or potential causes of adaptation. The planning component accommodates
`workflows that specify a partial order of actions which should be carried out
`in accordance with the results of the analysis component. And finally, the
`execution component controls the execution of such workflows and provides
`coordination if there are multiple concurrent workflows.
`
`1.2 Aspects of Self-Management
`
`A system is said to be self-managing if it exhibits one or more of the fol(cid:173)
`lowing characteristics: self-configuration, self-optimization, self-healing, and
`self-protecting.
`Self-configuration refers to the capability of a system to dynamically ad-
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`Context and Self-Management
`
`3
`
`just one or more parameters to accommodate expected or unexpected change
`within itself or in the operating environment. The change may be due to de(cid:173)
`parture, arrival or failure of a component; a change in the business policy of
`the user; or environmental, social or political constraints. A self-configuration
`capability of a system enables it to keep on functioning in the presence of con(cid:173)
`tinually changing and unforeseen obstacles.
`Self-optimization refers to the ability of a system to tune its parameters so
`that it can function most efficiently. Efficiency can be measured in terms of
`cost, quality of service, throughput, etc. A self-optimizing system improves
`its performance by finding, verifying and applying the latest software updates.
`Self-healing refers to the ability of a system to detect, localize, diagnose,
`and repair problems resulting from bugs or failures in software and hardware.
`Finally, self-protection refers to the ability of