Vasilakos / Mamei / Beraldi Autonomic Computing and Communications Systems

Third International ICST Conference, Autonomics 2009, Limassol, Cyprus, September 9-11, 2009, Revised Selected Papers
1. Auflage 2010
ISBN: 978-3-642-11482-3
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 23, 267 Seiten

Reihe: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

This book constitutes the thoroughly refereed post-conference proceedings of the thirs International ICST conference on Autonomic Computing and Communication Systems, Autonomics 2009, held in Cyprus Univesity of Technology, Limassol, Cyprus, in September 2009 The 11 revised full papers out of 26 submitted and hosted 5 invited talks, covering various aspects of autonomic computing including application, middelware, networking protocols and evaluation

Autoren/Hrsg.

Mamei, Marco

Friedman, Roy

Beraldi, Roberto

Vasilakos, Athanasios V.

Inhaltsverzeichnis

1;Preface;5
2;Organization;6
3;Table of Contents;9
4;A-OSGi: A Framework to Support the Construction of Autonomic OSGi-Based Applications;11
4.1;Introduction;11
4.2;Related Work;12
4.2.1;OSGi Platform;12
4.2.2;MAPE-K Control Loop;14
4.2.3;OSGi Monitoring;15
4.3;The A-OSGi Framework;15
4.4;Implementation of A-OSGi;17
4.4.1;Underlying Technologies;17
4.4.2;MAC Implementation;17
4.4.3;EC Implementation;19
4.4.4;KC Implementation;20
4.4.5;PEI Implementation;20
4.4.6;Framework Modifications;21
4.5;Evaluation;21
4.5.1;Using A-OSGi;22
4.5.2;Performance;22
4.5.3;Other Policies;25
4.6;Conclusions;25
4.7;References;25
5;A Network-Coding Based Event Diffusion Protocol for Wireless Mesh Networks;27
5.1;Introduction;27
5.1.1;Contribution of the Work;28
5.2;Background;29
5.2.1;Event Dissemination with Network Coding;31
5.3;Proposed Protocol;34
5.3.1;Basic Data Structure and Assumption;34
5.3.2;Protocol Description;35
5.3.3;Pull Phase;37
5.4;Evaluation;38
5.4.1;Protocol Tuning;38
5.4.2;Performance on a Grid;38
5.5;Conclusions;40
5.6;References;40
6;Expressing Adaptivity and Context Awareness in the ASSISTANT Programming Model;42
6.1;Introduction;42
6.2;Related Work;43
6.3;A Flood Management Application;44
6.4;Defining Parallel Versions;45
6.4.1;The ASSIST Model;48
6.4.2;Parallel Programs in ASSIST;48
6.4.3;Experiments;49
6.5;The ASSISTANT Programming Model;51
6.6;Programming Adaptivity for the Flood Application in ASSISTANT;54
6.7;Conclusions;56
6.8;References;56
7;Experiences in Benchmarking of Autonomic Systems;58
7.1;Introduction;58
7.2;Background;60
7.2.1;Models and Metrics;60
7.2.2;Benchmarking Methodologies and Tools;61
7.3;Assessment Methodology and Process;62
7.3.1;Methodology;62
7.3.2;Qualitative Assessment;63
7.3.3;Quantitative Assessment;63
7.3.4;Economical Assessment (Return on Investment);66
7.4;Experimental Results;67
7.4.1;Qualitative Assessment;67
7.4.2;Quantitative Assessment;69
7.4.3;Discussion;71
7.5;Conclusion;71
7.6;References;72
8;An Online Adaptive Model for Location Prediction;74
8.1;Introduction;74
8.2;Machine Learning Models;76
8.2.1;Offline kMeans;76
8.2.2;Online kMeans;77
8.2.3;Adaptive Resonance Theory;77
8.3;Context Representation;78
8.4;Mobility Prediction Model;78
8.5;Prediction Evaluation;81
8.5.1;Convergence of $C-T$ and $C-nT$;82
8.5.2;Precision of $C-T$ and $C-nT$;83
8.6;Comparison with Other Models;84
8.7;Prior Work;86
8.8;Conclusions;87
8.9;References;87
9;MPM: Map Based Predictive Monitoring for Wireless Sensor Networks;89
9.1;Introduction;89
9.2;Related Work;91
9.3;Preliminaries;92
9.3.1;System Model;92
9.3.2;Requirements on the MPM;92
9.3.3;Definitions;92
9.4;Predictive Monitoring: The MPM Approach;93
9.4.1;The Segmentation Phase;93
9.4.2;The Data Collection Phase;95
9.4.3;The Prediction Phase;95
9.4.4;The Event Detection Phase;97
9.5;Case Study: MPM Adaptation for Predicting Network Partition;98
9.5.1;Problem Formulation;98
9.5.2;The Segmentation Phase;99
9.5.3;The Data Collection Phase;99
9.5.4;The Prediction Phase;100
9.5.5;The Event (Holes/Partition) Detection Phase;100
9.6;Evaluation – Viability of Our Approach;100
9.6.1;Evaluation Metrics;100
9.6.2;Simulation Settings;101
9.6.3;Simulation Results;102
9.6.4;Discussion;104
9.7;Conclusion and Future Directions;104
9.8;References;104
10;Integrating Autonomic Grid Components and Process-Driven Business Applications;106
10.1;Introduction;106
10.2;The GCM Framework;107
10.2.1;Behavioural Skeletons;108
10.3;The ePVM Process Engine;111
10.4;Process-Driven Distributed Biometric Identification;112
10.4.1;Application Architecture;112
10.4.2;Process-Engine/GCM Interfacing;113
10.4.3;Using Autonomic GCM Components;114
10.4.4;Deployment and Component Creation;116
10.4.5;Application Monitoring;116
10.4.6;Automatic Futures vs. Message Passing;117
10.4.7;Integrated Development;118
10.5;Results, Experiences, and Lessons Learned;119
10.6;Conclusions;121
10.7;References;122
11;Using a Teleo-Reactive Programming Style to Develop Self-healing Applications;124
11.1;Introduction;124
11.2;Related Work;126
11.2.1;Teleo-Reactive Programs;127
11.3;Method;128
11.3.1;Goals;129
11.3.2;Conditions;129
11.3.3;Actions;130
11.3.4;Contrast;130
11.4;Design;132
11.4.1;Teleo-Reactive Program Design;132
11.4.2;Program Structure Design;133
11.5;Example;134
11.5.1;T-R Elements;135
11.5.2;Controls;136
11.5.3;Working through the Example;136
11.6;Future Work;137
11.7;Conclusion;138
11.8;References;138
12;Sensor Selection for IT Infrastructure Monitoring;140
12.1;Introduction;140
12.2;Related Work;143
12.3;Instrumentation Support of Metric Selection;144
12.4;Experimental Setup;145
12.5;Experimental Methodology;146
12.6;Experimental Results;148
12.7;Conclusion and Future Work;150
12.8;References;152
13;Context-Aware Self-optimization in Multiparty Converged Mobile Environments;154
13.1;Introduction;154
13.1.1;Related Work;155
13.2;Context-Aware Multiparty Service Provision;156
13.2.1;System Architecture;156
13.2.2;Context-Based Sub-grouping;158
13.3;Enabling Context-Awareness at the Session Level;159
13.3.1;Session Management Overview;159
13.3.2;Context-Aware Session Management;160
13.3.3;Initiating a Context-Aware Session;161
13.4;Enabling Context Awareness at the Network Level;162
13.4.1;Network Management Overview;162
13.4.2;Context-Aware Network Management;163
13.4.3;Grouping as a Part of Network Management;164
13.5;Propagating Context to Content;166
13.5.1;Motivation: Same User, Different Media Types, Different Sub-groups;166
13.5.2;The Importance of Sub-grouping;168
13.6;Conclusions and Future Work;168
13.7;References;169
14;Context Discovery in Mobile Environments: A Particle Swarm Optimization Approach;170
14.1;Introduction;170
14.2;Swarm Intelligence;172
14.3;The Context Discovery Problem;174
14.3.1;Context Representation and Quality of Context;174
14.3.2;Mapping Swarm Intelligence to Context Discovery;175
14.4;The Proposed Algorithm;177
14.4.1;Foraging for Context;177
14.4.2;Maintaining Fresh Context;179
14.4.3;The CDP Algorithm;179
14.5;Performance Evaluation;180
14.6;Conclusions;184
14.7;References;184
15;Consequences of Social and Institutional Setups for Occurrence Reporting in Air Traffic Organizations;186
15.1;Introduction;186
15.2;Modeling Formal Reporting in an ANSP;188
15.3;Modeling of Agents;191
15.3.1;Modeling Internal States and Interaction;191
15.3.2;Modeling Decision Making of a Controller Agent;193
15.4;Simulation Results;196
15.5;Sensitivity Analysis;198
15.6;Conclusions;200
15.7;References;201
16;Can Space Applications Benefit from Intelligent Agents?;202
16.1;Introduction;202
16.2;Swarm-Array Computing;205
16.2.1;Constituents;205
16.2.2;Approaches;207
16.3;Simulation Studies;208
16.3.1;Simulation Environment;208
16.3.2;Experimental Platform and Model;208
16.3.3;Simulation Results;209
16.4;Conclusion;210
16.5;References;210
17;A Generic Agent Organisation Framework for Autonomic Systems;213
17.1;Introduction;213
17.2;Related Work;216
17.2.1;Modelling Tasks;216
17.2.2;Modelling Organisational Characteristics;216
17.2.3;Modelling Agents;218
17.2.4;Evaluating an Organisation's Effectiveness;218
17.3;The Agent Organisation Framework;219
17.3.1;Task Representation;219
17.3.2;Organisation Representation;219
17.3.3;Organisation Performance Evaluation;222
17.4;Applying the Agent Organisation Framework;224
17.5;Conclusions;228
17.6;References;228
18;Metareasoning and Social Evaluations in Cognitive Agents;230
18.1;Introduction;230
18.2;Reasoning and Metareasoning: A Cognitive Approach;231
18.3;A Multicontext BDI Agent with Repage System;232
18.3.1;Preliminaries: Social Evaluations, Image and Reputation;232
18.3.2;The Multi-Context BDI Model;233
18.3.3;Belief Context (BC);233
18.3.4;Desire Context (DC);234
18.3.5;Intention Context (IC);235
18.3.6;Planner Context (PC) and Communication Context (CC);235
18.3.7;Repage Context (RC);235
18.3.8;Bridge Rules;236
18.4;The Metalevel Specification;237
18.4.1;DoF for Reasoning Rules;237
18.4.2;A Metalevel Specification for the Rules $A_{I}$ and $A_{R}$;238
18.4.3;A Metalevel Specification for the Trust Axiom;239
18.4.4;Processes Description;240
18.5;Experimentation;240
18.5.1;Scenario and Simulation Settings;241
18.5.2;Static Experiments;242
18.5.3;Dynamic Adaptation Experiments;243
18.6;Conclusions and Future Work;244
18.7;References;245
19;Experiments on the Acquisition of the Semantics and Grammatical Constructions Required for Communicating Propositional Logic Sentences;246
19.1;Introduction;246
19.2;Conceptualisation: Basic Definitions;248
19.2.1;Sensory Channels.;248
19.2.2;Logical Categories;249
19.3;Language Acquisition;251
19.4;Experiments;255
19.5;Intuitive Reasoning;259
19.6;Conclusions;260
19.7;References;260
20;An Autonomic Computing Architecture for Self-* Web Services;262
20.1;Introduction;262
20.2;Autonomic Computing and Web Services;263
20.2.1;Autonomic Computing;263
20.2.2;Self-* Properties in Web Services;264
20.3;Autonomic Web Service Architecture;265
20.4;Example of an Adaptation Scenario;267
20.5;Related Work;270
20.6;Implementation Issues;272
20.6.1;Behavioral Match;272
20.6.2;Complexity Management;273
20.6.3;Pragmatic Reasons;275
20.7;Conclusion and Future Work;276
20.8;References;276
21;Author Index;278