Andere Kunden interessierten sich auch für
![A Cost Model for Expressing and Estimating Ecological Costs of Software-Driven Systems]( "A Cost Model for Expressing and Estimating Ecological Costs of Software-Driven Systems")
A Cost Model for Expressing and Estimating Ecological Costs of Software-Driven Systems49,95 €![Energy-optimal routing with turn costs for electric vehicles]( "Energy-optimal routing with turn costs for electric vehicles")
Energy-optimal routing with turn costs for electric vehicles26,99 €![Cost Models for Parallel Programming on Shared Memory Architectures]( "Cost Models for Parallel Programming on Shared Memory Architectures")
Cost Models for Parallel Programming on Shared Memory Architectures24,99 €![Low-cost LIDAR]( "Low-cost LIDAR")
Low-cost LIDAR23,99 €![Intelligent Optimization Modelling in Energy Forecasting]( "Intelligent Optimization Modelling in Energy Forecasting")
Intelligent Optimization Modelling in Energy Forecasting90,99 €![Quantitative Risk Assessment & Management Metrics for Airport Services]( "Quantitative Risk Assessment & Management Metrics for Airport Services")
Quantitative Risk Assessment & Management Metrics for Airport Services32,99 €![Analysis of applications and success factors of cloud computing for small- and medium-sized businesses]( "Analysis of applications and success factors of cloud computing for small- and medium-sized businesses")
Analysis of applications and success factors of cloud computing for small- and medium-sized businesses24,99 €
The increasing popularity of cloud services today has led to a tremendous increase in energy consumption of data centers. As energy costs represent a significant part of the total cost of data center operations, energy cost reductions have become paramount for cloud providers to keep cloud services at an affordable price. This book provides insights into how to achieve significant energy cost reductions in environments with multiple interconnected data centers which feature connections to different power markets. The first part discusses various energy price forecasting methods in order to find the best method for predicting energy prices in power markets. The second part introduces a framework for cloud simulation in which different cloud conditions on real energy price data are tested. In addition, energy price forecasting methods are applied to further optimize resource scheduling and to maximize energy cost reductions. Finally, the results are summarized and the best model ispresented resulting from cloud simulations over a wide range of energy price data.