Neumann / Baker / Altmann Economic Models and Algorithms for Distributed Systems

1. Auflage 2010
ISBN: 978-3-7643-8899-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 270 Seiten

Reihe: Autonomic Systems

Distributed computing paradigms for sharing resources such as Clouds, Grids, Peer-to-Peer systems, or voluntary computing are becoming increasingly popular. While there are some success stories such as PlanetLab, OneLab, BOINC, BitTorrent, and SETI@home, a widespread use of these technologies for business applications has not yet been achieved. In a business environment, mechanisms are needed to provide incentives to potential users for participating in such networks. These mechanisms may range from simple non-monetary access rights, monetary payments to specific policies for sharing. Although a few models for a framework have been discussed (in the general area of a 'Grid Economy'), none of these models has yet been realised in practice. This book attempts to fill this gap by discussing the reasons for such limited take-up and exploring incentive mechanisms for resource sharing in distributed systems. The purpose of this book is to identify research challenges in successfully using and deploying resource sharing strategies in open-source and commercial distributed systems.

Autoren/Hrsg.

Neumann, Dirk

Baker, Mark

Altmann, Jörn

Rana, Omer F.

Inhaltsverzeichnis

1;Table of Content ;6
2;Economic Models and Algorithms forDistributed Systems;8
3;Part I: Reputation Mechanisms and Trust;11
3.1;Reputation Mechanisms and Trust;12
3.2;A Belief-based Trust Model for DynamicService Selection;14
3.2.1;1. Introduction;14
3.2.2;2. Motivations;15
3.2.3;3. Related work;15
3.2.4;4. The methodology;17
3.2.5;5. Trust components;17
3.2.5.1;5.1 The sources of beliefs;18
3.2.6;6. Illustrating beliefs;19
3.2.7;7. Deriving a trust formalism;19
3.2.7.1;7.1 Combining belief values from various sources;19
3.2.7.2;7.2 Weighted Dempster–Shafer theory;21
3.2.7.3;7.3 Trust adaptation: Dynamic weighting;22
3.2.7.4;7.4 Trust computation and selection;23
3.2.8;8. Empirical evaluation;23
3.2.8.1;8.1 Environment overview;23
3.2.8.2;8.2 Setup summary;25
3.2.8.3;8.3 Results;26
3.2.8.3.1;8.3.1 Simulation 1: Service selection without trust;26
3.2.8.3.2;8.3.2 Simulation 2: Service selection with trust;26
3.2.8.3.3;8.3.3 Simulation 3: Full service adjustment;26
3.2.8.3.4;8.3.4 Simulation 4: Delayed service adjustment;27
3.2.8.4;8.4 Discussion;27
3.2.9;9. Conclusion and future work;27
3.2.10;References;27
3.3;Reputation, Pricing and the E-Science Grid;29
3.3.1;1. Introduction;29
3.3.2;2. Offline allocation with fixed price;31
3.3.2.1;2.1 Scenario;32
3.3.2.2;2.2 Model;32
3.3.3;3. Reputation-based scheduling and pricing for online allocation;34
3.3.3.1;3.1 Scenario;34
3.3.3.2;3.2 Model;36
3.3.3.3;3.3 Parameter;37
3.3.3.4;3.4 Sellers’ and buyers’ action space;38
3.3.3.5;3.5 Reputation mechanism;40
3.3.4;4. Simulation and implementation;41
3.3.4.1;4.1 Setting;41
3.3.4.2;4.2 Results;42
3.3.4.3;4.3 Application;43
3.3.5;5. Conclusion;44
3.3.6;References;45
3.4;Trust-oriented Utility-based CommunityStructure in Multiagent Systems;48
3.4.1;1. Introduction;48
3.4.2;2. The approach;49
3.4.2.1;2.1 Communities reasoning about agents;50
3.4.2.1.1;2.1.1 Modeling the trustworthiness of agents;51
3.4.2.1.2;2.1.2 Incentives for communities to share reputation ratings of agents;52
3.4.2.1.3;2.1.3 Interpreting ratings provided by communities;57
3.4.2.1.4;2.1.4 Overview of community reasoning procedure;58
3.4.2.2;2.2 Agents reasoning about communities;59
3.4.2.3;2.3 Privacy considerations;60
3.4.3;3. Discussion;61
3.4.4;4. Future work;62
3.4.5;References;63
3.5;Formation of Virtual Organizations in Grids:A Game-Theoretic Approach;65
3.5.1;1. Introduction;65
3.5.1.1;1.1 Our contributions;67
3.5.1.2;1.2 Related work;67
3.5.1.3;1.3 Organization;68
3.5.2;2. Coalitional game theory;68
3.5.3;3. Model;70
3.5.4;4. Virtual organization formation;71
3.5.5;5. Virtual organization formation framework;77
3.5.6;6. Conclusion;80
3.5.7;References;81
3.6;Towards Dynamic Authentication inthe Grid – Secure and Mobile BusinessWorkflows Using GSet;84
3.6.1;1. Introduction;84
3.6.2;2. State of the art;86
3.6.3;3. Requirements analysis – The BIG project;88
3.6.4;4. The need for dynamic authorization;89
3.6.5;5. Gridified Secure Electronic Transaction (gSET);90
3.6.5.1;5.1 gSET;91
3.6.5.2;5.2 Architecture;92
3.6.6;6. Scenario and business model;93
3.6.7;7. Integrating gSET with a mobile client;94
3.6.7.1;7.1 Considerations regarding mobile devices;95
3.6.7.2;7.2 Tickets;96
3.6.7.3;7.3 The mobile gSET workflow;96
3.6.8;8. Performance analysis;98
3.6.8.1;8.1 gSET versus gridmap;98
3.6.8.2;8.2 Evaluation of gSET in a real mobile grid environment;100
3.6.9;9. Conclusions and future work;102
3.6.10;References;102
4;Part II: Service Level Agreements;106
4.1;Service Level Agreements;107
4.2;Enforcing Service Level Agreements Using anEconomically Enhanced Resource Manager;109
4.2.1;1. Introduction;109
4.2.2;2. Related work;110
4.2.3;3. Scenario definition;112
4.2.3.1;3.1 Revenue maximisation in resource-limited providers;113
4.2.3.2;3.2 SLA violation;114
4.2.4;4. Economically Enhanced Resource Manager;115
4.2.4.1;4.1 Architecture;116
4.2.4.2;4.2 Economic Resource Manager (ERM);117
4.2.4.3;4.3 Monitoring;118
4.2.4.4;4.4 SLA enforcement;120
4.2.5;5. Example scenario;121
4.2.6;6. Conclusions and future work;123
4.2.7;References;124
4.3;Extended Resource Management Using ClientClassification and Economic Enhancements;128
4.3.1;1. Introduction;128
4.3.2;2. Objectives;129
4.3.3;3. Motivational scenario;130
4.3.4;4. Related work;130
4.3.5;5. Economic enhancements and client classification;131
4.3.6;6. Economically Enhanced Resource Management;132
4.3.6.1;6.1 Economic design criteria;132
4.3.6.2;6.2 Model of the EERM;133
4.3.7;7. Evaluation;135
4.3.8;8. Conclusions;137
4.3.9;References;137
4.4;Mitigating Provider Uncertainty inService Provision Contracts;141
4.4.1;1. Introduction and motivation;142
4.4.2;2. Related work;143
4.4.3;3. Utility model for contract-based service provision;144
4.4.4;4. Negative consequences of inaccurate quality level estimators;146
4.4.5;5. Performance prediction methods for derivation of qualitylevel estimators;148
4.4.6;6. Results;150
4.4.7;7. Implementation;153
4.4.8;8. Conclusion;156
4.4.9;References;156
4.5;Text-Content-Analysis based on the SyntacticCorrelations between Ontologies;158
4.5.1;1. Introduction;159
4.5.2;2. Description of work;160
4.5.2.1;2.1 Analyzing ontologies;161
4.5.2.2;2.2 Text-Content-Analysis;163
4.5.2.2.1;2.2.1 Matching algorithm;164
4.5.2.2.2;2.2.2 TCA usage;167
4.5.2.3;2.3 SLA-Management-System;169
4.5.2.4;2.4 A useful service;174
4.5.3;3. Conclusions;175
4.5.4;References;176
5;Part III: Business Models and Market Mechanisms;178
5.1;Business Models and Market Mechanisms;179
5.2;Cloud Computing Value Chains:Understanding Businesses and Value Creation inthe Cloud;182
5.2.1;1. Introduction;183
5.2.2;2. Literature review;184
5.2.2.1;2.1 Porter value chain;184
5.2.2.2;2.2 Upgrading the value chain;184
5.2.2.3;2.3 State-of-the-art: Cloud value chains;185
5.2.3;3. The cloud value chain reference model;186
5.2.3.1;3.1 Data and methodology;186
5.2.3.2;3.2 Model;186
5.2.3.3;3.3 Model structure and relations;189
5.2.3.4;3.4 Service scenarios;190
5.2.3.4.1;3.4.1 Utility cloud;191
5.2.3.4.2;3.4.2 Enterprise cloud;191
5.2.3.4.3;3.4.3 Research grids;192
5.2.3.4.4;3.4.4 Public clouds (desktop grids);192
5.2.3.4.5;3.4.5 Virtual clouds (VC);193
5.2.4;4. Discussion and policy implications;193
5.2.5;5. Conclusion;194
5.2.6;References;200
5.3;A Model for Determining the Optimal CapacityInvestment for Utility Computing;204
5.3.1;1. Introduction;204
5.3.2;2. Literature review;205
5.3.3;3. A decision model;207
5.3.4;4. Base model;208
5.3.5;5. Simultaneous optimization of capacity and demand;210
5.3.6;6. Analysis of model behaviors;211
5.3.7;7. Conclusion;213
5.3.8;References;214
5.4;A Combinatorial Exchange for ComplexGrid Services;216
5.4.1;1. Introduction;217
5.4.2;2. Related work;217
5.4.3;3. Application scenario: Collaborative learning;218
5.4.4;4. A combinatorial exchange;219
5.4.4.1;4.1 Bidding specification;219
5.4.4.2;4.2 The allocation mechanism;221
5.4.4.3;4.3 The pricing;224
5.4.5;5. Evaluation;225
5.4.5.1;5.1 Data generation;226
5.4.5.2;5.2 Analysis;226
5.4.6;6. Summary and future work;229
5.4.7;References;230
5.5;Heuristic Scheduling in Grid Environments:Reducing the Operational Energy Demand;233
5.5.1;1. Introduction;233
5.5.2;2. Related work;235
5.5.3;3. The model;236
5.5.3.1;3.1 The setting;236
5.5.3.2;3.2 Optimal solution;239
5.5.3.3;3.3 Green heuristic;240
5.5.4;4. Strategic incentives;243
5.5.4.1;4.1 Misreporting resource characteristics;243
5.5.4.2;4.2 Misreporting valuations;244
5.5.4.2.1;4.2.1 Misreporting energy costs;244
5.5.4.2.2;4.2.2 Misreporting bids;245
5.5.4.2.3;4.2.3 Misreporting deadlines;246
5.5.5;5. Conclusion;247
5.5.6;References;247
5.6;Facing Price Risks inInternet-of-Services Markets;251
5.6.1;1. Introduction;251
5.6.2;2. Background and related work;253
5.6.2.1;2.1 Background of cloud computing markets;253
5.6.2.2;2.2 Related work on upcoming risks;255
5.6.2.2.1;2.2.1 Technical risk;255
5.6.2.2.2;2.2.2 Price risk;256
5.6.3;3. Quantifying and overcoming risk;257
5.6.3.1;3.1 Simulating cloud computing markets;257
5.6.3.2;3.2 Applying an option price model to the simulated cloud computing market;258
5.6.4;4. Conclusion;259
5.6.5;References;260