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) ...
Read More
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.
Read Less
Add this copy of Economic Models for Managing Cloud Services to cart. $9.81, new condition, Sold by Red's Corner rated 5.0 out of 5 stars, ships from Tucker, GA, UNITED STATES, published 2018 by Springer.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
New. All orders ship by next business day! For USED books, we cannot guarantee supplemental materials such as CDs, DVDs, access codes and other materials. We are a small company and very thankful for your business!
Add this copy of Economic Models for Managing Cloud Services to cart. $45.67, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Newport Coast, CA, UNITED STATES, published 2018 by Springer.
Add this copy of Economic Models for Managing Cloud Services to cart. $49.15, like new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2018 by Springer.
Choose your shipping method in Checkout. Costs may vary based on destination.
Seller's Description:
Fine. Sewn binding. Cloth over boards. 141 p. Contains: Illustrations, black & white, Illustrations, color. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
