Slezak / Kim / Yau Grid and Distributed Computing

International Conference, GDC 2009, Held as Part of the Future Generation Information Technology Conferences, FGIT 2009, Jeju Island, Korea, December 10-12, 2009, Proceedings
1. Auflage 2010
ISBN: 978-3-642-10549-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 63, 240 Seiten

Reihe: Communications in Computer and Information Science

This book constitutes the proceedings of the International Conference on Grid and Distributed Computing, GDC 2009, held as part of the Future Generation Information Technology Conferences, FGIT 2009, held on Jeju Island, Korea in December 2009. The 26 papers presented in this volume were carefully reviewed and selected from numerous submissions.

Autoren/Hrsg.

Slezak, Dominik

Kim, Tai-Hoon

Yau, Stephen S. T.

Gervasi, Osvaldo

Kang, Byeong-Ho

Inhaltsverzeichnis

1;Title Page;2
2;Foreword;5
3;Preface;7
4;Organization;8
5;Table of Contents;9
6;Autonomic Management of Object Replication for FT-CORBA Based Intelligent Transportation Systems;12
6.1;Introduction;12
6.2;Related Work;13
6.3;Proposed Architecture;13
6.4;Evaluations;17
6.5;Conclusion;19
6.6;References;19
7;Meshlization of Irregular Grid Resource Topologies by Heuristic Square-Packing Methods;20
7.1;Introduction;20
7.2;Proposed Algorithms;21
7.3;Experiment Results;24
7.4;Conclusions;27
7.5;References;27
8;An Architecture and Supporting Environment of Service-Oriented Computing Based-On Context Awareness;28
8.1;Introduction;28
8.2;Context Aware Architecture;29
8.2.1;Light-Weight Directory Service;30
8.2.2;Service Discovery and On-Demand Aggregation Based on Context;30
8.2.3;Dynamic Evolvement Based on Context Awareness;31
8.3;Service Context Ontology;31
8.4;Supporting Environment of Context-Aware Service Composition;32
8.5;Reference Implementation;33
8.6;Related Work;33
8.7;Conclusion;34
8.8;References;35
9;Relaxed Time Slot Negotiation for Grid Resource Allocation;36
9.1;Introduction;36
9.2;Relaxed Time Slot Negotiation;37
9.2.1;Decision Making Model;37
9.2.2;Negotiation Protocol;39
9.3;Simulations and Empirical Results;39
9.4;Conclusion;43
9.5;References;43
10;A Brokering Protocol for Agent-Based Grid Resource Discovery;44
10.1;Introduction;44
10.2;Connecting User and Provider;45
10.3;Simulations and Empirical Results;47
10.4;Conclusion and Future Work;50
10.5;References;51
11;Towards a Better Understanding of Locality-Awareness in Peer-to-Peer Systems;52
11.1;Introduction;52
11.2;Multi-Metric Neighbor Selection Algorithm;53
11.2.1;Physical vs. Logical Network;53
11.2.2;AS/ISP Filter and Geo Filter;54
11.3;Network Benefit Model;54
11.4;Evaluation;56
11.5;Conclusions;59
11.6;References;59
12;Default a-Logic for Modeling Customizable Failure Semantics in Workflow Systems Using Dynamic Reconfiguration Constraints;60
12.1;Introduction;60
12.2;Background on Default Logic;61
12.3;Overview of Failure Handling in Workflow Systems;62
12.4;Default a-Logic;63
12.5;A Case Study of Failure Modeling in Workflow Systems Using Dynamic Reconfiguration Constraints;64
12.5.1;An Example Scenario;65
12.6;References;66
13;A Back Propagation Neural Network for Evaluating Collaborative Performance in Cloud Computing;68
13.1;Introduction;68
13.2;Proposed Evaluation Approach;69
13.2.1;Notations;69
13.2.2;Proposed BPNN Model;70
13.3;Simulation Results;71
13.3.1;Fixed Number of Participants;72
13.3.2;Variable Number of Participants;73
13.4;Conclusion;74
13.5;References;75
14;SCAIMO – A Case for Enabling Security in Semantic Web Service Composition;76
14.1;Introduction;76
14.2;Related Work;77
14.3;Security Capability and Constraint Types in SCAIMO;78
14.4;SCAIMO Architecture;80
14.5;Case Study;81
14.6;Conclusion and Future Work;82
14.7;References;82
15;Automatic Synthesis and Deployment of Intensional Kahn Process Networks;84
15.1;Introduction;84
15.2;Related Work;86
15.3;Our Approach;88
15.3.1;Example Application;90
15.3.2;Intensional Kahn Processes;91
15.3.3;Logic for Intensional Kahn Processes;93
15.4;Synthesis of Intensional Kahn Processes;94
15.5;Conclusions;96
15.6;References;96
16;Extended Heartbeat Mechanism for Fault Detection Service Methodology;99
16.1;Introduction;99
16.2;Extended Heartbeat Mechanism;100
16.2.1;Integrating Index Server with Extended Heartbeat Mechanism;100
16.2.2;Utilising Ping Service with Extended Heartbeat Mechanism;102
16.3;The Performance;102
16.3.1;The FDS Performance;102
16.3.2;The Performance of the Proposed Model;104
16.3.3;Performance Comparison;105
16.4;Research Findings;105
16.5;Conclusion;106
16.6;References;106
17;Trust-Oriented Multi-objective Workflow Scheduling in Grids;107
17.1;Introduction;107
17.2;Related Work;108
17.3;Grid Trust Model;109
17.4;Trust-Oriented Multi-objective Scheduling Problem;111
17.4.1;Workflow Application Model (WAM);111
17.4.2;Grid Resource Model (GRM);111
17.4.3;Performance Criteria;112
17.5;Trust-Oriented Multi-objective Scheduling Algorithm;113
17.5.1;Primary Scheduling;113
17.5.2;Secondary Scheduling;114
17.6;Simulation and Experimental Results;115
17.7;Conclusions;117
17.8;References;117
18;Empirical Comparison of Race Detection Tools for OpenMP Programs;119
18.1;Introduction;119
18.2;Background;120
18.2.1;The Races in OpenMP Programs;121
18.2.2;Race Detection Tools;121
18.3;Experimentation;122
18.3.1;Design of Synthetic Programs;123
18.3.2;Experimentation Methods;123
18.4;Analysis of Results;124
18.4.1;Verification Capability;124
18.4.2;Efficiency;125
18.5;Conclusion;126
18.6;References;127
19;Efficient Service Recommendation System for Cloud Computing Market;128
19.1;Introduction;128
19.2;Related Work;129
19.2.1;Recommendation System;129
19.2.2;Cloud Computing Market;130
19.3;Architecture Recommendation System Based Cloud Market;130
19.3.1;Cloud Resource Recommendation System;130
19.3.2;Resource Register to the Cloud Market;131
19.3.3;Resource Rank Analysis;132
19.4;Conclusion;134
19.5;References;135
20;Scalable Cooperative Positioning System in Wireless Sensor Networks;136
20.1;Introduction;136
20.2;Relative Studies;137
20.3;SCOPS;138
20.3.1;Improved Positioning Convergence Adjustment Factor;138
20.3.2;Convergence Acceleration Factor;139
20.4;Simulation and Results;140
20.5;Conclusions;142
20.6;References;143
21;One-to-One Embedding between Hyper Petersen and Petersen-Torus Networks;144
21.1;Introduction;144
21.2;Related Work;145
21.3;Embedding Hyper Petersen into Petersen-Torus;147
21.4;Conclusion;149
21.5;References;150
22;A Dynamic Mobile Grid System for 4G Networks;151
22.1;Introduction;151
22.2;Service Composition for Grid Systems in 4G Networks;153
22.3;Service Assignment Policy for 4G Networks;155
22.4;Towards a Service Continuity Model;157
22.5;Conclusion;157
22.6;References;157
23;Authorization Framework for Resource Sharing in Grid Environments;159
23.1;Introduction;159
23.2;Injecting Ramars Framework to Grid Environments;161
23.2.1;Integrated RamarsAuthZ System;163
23.2.2;Enhanced DRS for Access Control;164
23.3;Performance Evaluation;165
23.4;Conclusion;165
23.5;References;166
24;Design and Implementation of a SOA-Based Medical Grid Platform;167
24.1;Introduction;167
24.2;System Design and Implement;168
24.2.1;Architecture;168
24.2.2;Resource Broker;168
24.2.3;SOA Web Portal;169
24.2.4;SOA Technology Combination Resource Broker;170
24.2.5;Services;172
24.3;Experiment Results;172
24.4;Conclusion and Future Work;173
24.5;References;173
25;RFID-Based Onion Skin Location Estimation Technique in Indoor Environment;175
25.1;Introduction;175
25.2;Related Work;177
25.3;Target Environment and Problem Definition;178
25.3.1;Target Environment;178
25.3.2;Problem Definition;179
25.4;Model and Algorithm;180
25.4.1;Location Estimation Model;180
25.4.2;Onion Skin Location Estimation (OSLE) Algorithm;182
25.5;Conclusion and Future Work;185
25.6;References;186
26;Harmonized Media Service Middleware Using to Emotional Knowledge;187
26.1;Introduction;187
26.2;Related Works;188
26.3;Harmonized Media Service Middleware;188
26.3.1;Formation of Harmonized Media Service Middleware;189
26.4;Algorithm;190
26.4.1;Analyzer;190
26.4.2;Extractor;191
26.4.3;Harmonizer;192
26.5;Realization of the HMSM;192
26.6;Conclusions;193
26.7;References;194
27;A Trust Evaluation Model for Cloud Computing;195
27.1;Introduction;195
27.2;Trust Model Implementation;196
27.3;Experiments;197
27.3.1;System Configuration;197
27.3.2;Performance Metrics;198
27.3.3;Experimental Results;200
27.4;Conclusions;202
27.5;References;203
28;Multiple Reduced Hypercube MRH(n): A New Interconnection Network Reducing Both Diameter and Edge of Hypercube;204
28.1;Introduction;204
28.2;Preliminaries;205
28.3;Design of Multiple Reduced Hypercube (MRH(n));206
28.3.1;Definition of Multiple Reduced Hypercube;206
28.3.2;Routing Algorithm and Diameter;208
28.4;Comparative Analysis with Other Interconnection Networks;214
28.5;Conclusion;215
28.6;References;216
29;Embedding Algorithm between MRH(n) and Hypercube;217
29.1;Introduction;217
29.2;Preliminaries;218
29.3;Embedding between Qn and MRH(n);220
29.4;Conclusion;224
29.5;References;224
30;Fuzzy Based Approach for Load Balanced Distributing Database on Sensor Network;226
30.1;Introduction;226
30.2;Fuzzy Sets Overview;227
30.3;Startphase;228
30.4;Simulation Phase;228
30.4.1;The Fuzzy Reflexivity Module;228
30.4.2;The Fuzzy Symmetry Module;228
30.4.3;Partition Forming State;229
30.5;Conclusion;231
30.6;References;231
31;An Ontology-Based Resource Selection Service on Science Cloud;232
31.1;Introduction;232
31.2;Ontology-Based Resource Selection Service(OReSS);233
31.2.1;Architecture of OreSS;233
31.2.2;Resource Selection Mechanism in OReSS;234
31.3;Scientific Applications Execution Scenario on Science Cloud;234
31.4;Experiments;235
31.5;Conclusion and Future Work;238
31.6;References;239
32;Author Index;240

Leseproben

"A Dynamic Mobile Grid System for 4G Networks (p. 140-141)

Abstract. Future networks specially International Mobile Telecommunications- advanced, better known as 4G, will come up with a panoply of services so as to provide a comprehensive and secure IP-based solution where facilities such as voice, data and stremed multimedia will be provided to users anywhere at anytime.

This solution will also provide much higher data rates compared to previous generations. More importantly, the 4G architecture will strongly promote ubiquitous computing, which involves many computational devices and systems simultaneously. Such devices and systems can even be unaware that they are contributing to computational process. Grid systems have been known in traditional networks as an e?cient solution to provide distributed services.

However, their applicability to 4G networks is not straightforward because of the intrinsic features of serveral network categories. In this paper, we propose a new Grid architecture suitable with the characteristics of 4G networks. A particular emphasis will be put on the provision of ubiquitous computational services in and across ad hoc environments. We de?ne a new relational model enabling Grid service composing.

1 Introduction

Mobile networks are witnessing a prominent development. They are changing from being simple access means to value added service providers. In fact, considering the growing mobile population and the induced raw and unexploited resources, important applications and services could be de?ned based on the infrastructure provided by mobile networks. The advent of 4G networks will probably promote the development of more sophisticated services.

The architecture of these networks, mainly their cooperative nature, will allow the implementation of ubiquitous computational frameworks. In fact, 4G networks build bridges between traditionally heterogeneous networks (e.g., WLAN, UMTS, WSN) in a manner that the shortcuts of a network are overcame by the strengthes of other networks.

For instance, the short range characterizing ad hoc networks can be extended using mobile backbones (possibly constituted by cellular segments). This allows taking bene?t of the high transmission rates available through ad hoc networks over long ranges. Grid [1] is a distributed system composed of various, heterogeneous, autonomous and distributed resources such as processes, computing applications,"