| Description |
1 online resource |
| Contents |
Intro; Foreword; Preface; Acknowledgments; Contents; List of Figures; List of Tables; 1 Introduction; 1.1 Cloud Service Management; 1.1.1 Cloud Management Using Service Composition; 1.2 Economic Models for Better Cloud Service Management; 1.2.1 Challenges in Developing a Quantitative Economic Model; 1.2.2 Challenges in Developing a Qualitative Economic Model; 1.2.3 Economic Model Based Cloud Service Composition; 1.3 Outline of the Book Chapters; 2 Background; 2.1 Cloud Service Management from an End User's Perspective; 2.1.1 Service Composition with Functional Requirements |
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2.1.2 Service Composition with Non-functional Requirements2.1.3 Service Composition with Long-Term Requirements; 2.2 Cloud Service Management from a Provider's Perspective; 2.2.1 Resource Allocation Approaches; 2.2.2 Task Scheduling Approaches; 2.2.3 Admission Control Approaches; 2.3 Economic Models; 2.3.1 Economic Modeling in Operations Research; 2.3.2 Quantitative Economic Modeling in the Cloud Market; 2.3.3 Qualitative Economic Modeling in the Cloud Market; 2.4 Prediction Modeling in Service Composition; 2.4.1 Time-Series and Probabilistic Prediction Models |
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2.4.2 Web Service QoS Prediction Frameworks2.5 Optimization Approaches in Service Composition; 2.5.1 Global Optimization Approaches; 2.5.2 Sequential Local Optimization and Machine-Learning Approaches; 2.6 Conclusion; 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; 3.4.1 Predicting Runtime Behavior of Existing Consumers' Requests; 3.4.1.1 Multivariate HMM Modeling of High-Frequent Usage Patterns |
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3.4.1.2 HMM-ARIMA Modeling of Seasonal Usage Patterns3.4.1.3 Selection of Models; 3.4.2 Predicting Runtime Behavior of New Consumers' Requests; 3.5 An ILP Modeling for Request Optimization; 3.6 Experiments and Results; 3.6.1 Data Description; 3.6.1.1 Correlation Density Index (CDI) in the Dataset; 3.6.1.2 Setup of Economic Values for Profit Modeling; 3.6.2 Accuracy in Predicting the Behavior of Consumer Requests; 3.6.3 Performance Analysis on Profit Maximization; 3.7 Conclusion; 4 Long-Term IaaS Composition for Stochastic Requests; 4.1 Introduction |
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4.2 Long-Term Dynamic IaaS Composition Framework4.3 Long-Term Economic Model of IaaS Provider; 4.3.1 Long-Term Economic Valuation; 4.3.2 Semantic Economic Expectation and Fitnessof a Composition; 4.4 Genetic Optimization Using IaaS Economic Model; 4.5 Hybrid Adaptive Genetic Algorithm (HAGA) Based Composition; 4.5.1 Solution Representation in HAGA; 4.5.2 Initial Population Generation; 4.5.3 Parent Selection, Crossover, and Mutation; 4.5.4 Solution Generation with Repair Heuristic; 4.5.5 Runtime Optimization Scheduling; 4.6 Experiments and Results; 4.6.1 Data Description |
| Summary |
The authors introduce both the quantitative and qualitative economic models as optimization tools for the selection of long-term cloud service requests. The economic models fit almost intuitively in the way business is usually done and maximize the profit of a cloud provider for a long-term period. The authors propose a new multivariate Hidden Markov and Autoregressive Integrated Moving Average (HMM-ARIMA) model to predict various patterns of runtime resource utilization. A heuristic-based Integer Linear Programming (ILP) optimization approach is developed to maximize the runtime resource utilization. It deploys a Dynamic Bayesian Network (DBN) to model the dynamic pricing and long-term operating cost. A new Hybrid Adaptive Genetic Algorithm (HAGA) is proposed that optimizes a non-linear profit function periodically to address the stochastic arrival of requests. Next, the authors explore the Temporal Conditional Preference Network (TempCP-Net) as the qualitative economic model to represent the high-level IaaS business strategies. The temporal qualitative preferences are indexed in a multidimensional k-d tree to efficiently compute the preference ranking at runtime. A three-dimensional Q-learning approach is developed to find an optimal qualitative composition using statistical analysis on historical request patterns. Finally, the authors propose a new multivariate approach to predict future Quality of Service (QoS) performances of peer service providers to efficiently configure a TempCP-Net. It discusses the experimental results and evaluates the efficiency of the proposed composition framework using Google Cluster data, real-world QoS data, and synthetic data. It also explores the significance of the proposed approach in creating an economically viable and stable cloud market. This book can be utilized as a useful reference to anyone who is interested in theory, practice, and application of economic models in cloud computing. This book will be an invaluable guide for small and medium entrepreneurs who have invested or plan to invest in cloud infrastructures and services. Overall, this book is suitable for a wide audience that includes students, researchers, and practitioners studying or working in service-oriented computing and cloud computing |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed February 15, 2018) |
| In |
Springer eBooks |
| Subject |
Cloud computing -- Management -- Economic aspects
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Information architecture.
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Network hardware.
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Information retrieval.
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COMPUTERS -- Computer Literacy.
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COMPUTERS -- Computer Science.
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COMPUTERS -- Data Processing.
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COMPUTERS -- Hardware -- General.
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COMPUTERS -- Information Technology.
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COMPUTERS -- Machine Theory.
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COMPUTERS -- Reference.
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Computer networks
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Computer science
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Management information systems
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| Form |
Electronic book
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| Author |
Bouguettaya, Athman, author.
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Dong, Hai, author
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| LC no. |
2017964374 |
| ISBN |
9783319738765 |
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3319738763 |
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