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Economic Models for Managing Cloud Services


Economic Models for Managing Cloud Services
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Beschreibung

1              Introduction      

1.1          Cloud Computing            

1.2          Cloud Service Models   

1.3          Provider Centered Cloud Service Computing      

1.4          Use Cases: Cloud Service Composition

1.5          Key Research Challenges             

1.4          Research Contributions

1.5          Organization

2              Cloud Service Composition: The State of the Art               

2.1          Cloud Service Composition from an End User's Perspective

2.2          Cloud Service Composition from a Provider's Perspective            

2.3          Economic Models           

2.4          Prediction Modeling in Service Composition       

2.5          Optimization Approaches in Service Composition             

3              Long-term IaaS Composition for Deterministic Requests

3.1          Introduction

3.2          The Heuristics on Consumer Behavior

3.3          The Long-term Composition Framework for Deterministic Requests

3.4          Predicting the Dynamic Behavior of Consumer Requests              

3.5          An ILP Modeling for Request Optimization

3.6          Experiments and Results

4              Long-term IaaS Composition for Stochastic Requests

4.1          Introduction      

4.2          Long-term Dynamic IaaS Composition Framework

4.3          Long-term Economic Model of IaaS Provider

4.4          Genetic Optimization using IaaS Economic Model            

4.5          Hybrid Adaptive Genetic Algorithm (HAGA) based Composition                

4.6          Experiments and Results                             

5              Long-term Qualitative IaaS Composition               

5.1          Introduction      

5.2          Motivation:  A Qualitative IaaS Economic Model with Decision Variables

5.3          The Temporal CP-Net based Qualitative Economic Model            

5.4          Optimization Algorithms for Qualitative IaaS Composition            

5.5          Reinforcement Learning for Long-term IaaS   Requests Composition                       

5.6          Experiments and Results                                             

6              Service Providers' Long-term QoS Prediction Model       

6.1          Introduction      

6.2          The Multivariate QoS Forecasting Framework    

6.3          Multivariate QoS Prediction Model (MQPM)      

6.4          Forecasting from the MQPM

6.5          Experiments and Results

7              Conclusion

7.1          Future Work      

Eigenschaften

Breite: 163
Gewicht: 422 g
Höhe: 247
Länge: 13
Seiten: 141
Sprachen: Englisch
Autor: Athman Bouguettaya, Hai Dong, Sajib Mistry

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