ISBN-10:
3319738755
ISBN-13:
9783319738758
Pub. Date:
02/11/2018
Publisher:
Springer International Publishing
Economic Models for Managing Cloud Services

Economic Models for Managing Cloud Services

Hardcover

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Overview

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.

Product Details

ISBN-13: 9783319738758
Publisher: Springer International Publishing
Publication date: 02/11/2018
Edition description: 1st ed. 2018
Pages: 141
Product dimensions: 6.10(w) x 9.25(h) x (d)

Table of Contents

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

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