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Economic models for managing cloud s...

Mistry, Sajib.

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Economic models for managing cloud services

Record Type:	Electronic resources : Monograph/item
Title/Author:	Economic models for managing cloud services/ by Sajib Mistry, Athman Bouguettaya, Hai Dong.
Author:	Mistry, Sajib.
other author:	Bouguettaya, Athman.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xix, 141 p. :ill., digital ;24 cm.
[NT 15003449]:	1 Introduction -- 2 Cloud Service Composition: The State of the Art -- 3 Long-term IaaS Composition for Deterministic Requests -- 4 Long-term IaaS Composition for Stochastic Requests -- 5 Long-term Qualitative IaaS Composition -- 6 Service Providers' Long-term QoS Prediction Model -- 7 Conclusion.
Contained By:	Springer eBooks
Subject:	Cloud computing - Management -
Online resource:	http://dx.doi.org/10.1007/978-3-319-73876-5
ISBN:	9783319738765

Economic models for managing cloud services
Mistry, Sajib.

Economic models for managing cloud services[electronic resource] /by Sajib Mistry, Athman Bouguettaya, Hai Dong. - Cham :Springer International Publishing :2018. - xix, 141 p. :ill., digital ;24 cm.

1 Introduction -- 2 Cloud Service Composition: The State of the Art -- 3 Long-term IaaS Composition for Deterministic Requests -- 4 Long-term IaaS Composition for Stochastic Requests -- 5 Long-term Qualitative IaaS Composition -- 6 Service Providers' Long-term QoS Prediction Model -- 7 Conclusion.

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.

ISBN: 9783319738765

Standard No.: 10.1007/978-3-319-73876-5doiSubjects--Topical Terms:

3301612
Cloud computing
--Management

LC Class. No.: QA76.585

Dewey Class. No.: 004.6782

Economic models for managing cloud services
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100 1 $a Mistry, Sajib. $3 3301611
245 1 0 $a Economic models for managing cloud services $h [electronic resource] / $c by Sajib Mistry, Athman Bouguettaya, Hai Dong.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xix, 141 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 Introduction -- 2 Cloud Service Composition: The State of the Art -- 3 Long-term IaaS Composition for Deterministic Requests -- 4 Long-term IaaS Composition for Stochastic Requests -- 5 Long-term Qualitative IaaS Composition -- 6 Service Providers' Long-term QoS Prediction Model -- 7 Conclusion.
520 $a 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.
650 0 $a Cloud computing $x Management $x Economic aspects. $3 3301612
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Information Systems Applications (incl. Internet) $3 1565452
650 2 4 $a Management of Computing and Information Systems. $3 892490
650 2 4 $a Computer Communication Networks. $3 775497
700 1 $a Bouguettaya, Athman. $3 1005624
700 1 $a Dong, Hai. $3 1946633
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-73876-5
950 $a Computer Science (Springer-11645)