Chapter
Chapter 2: Cloud Computing in Business
2.1.1. Software-as-a-Service (SaaS)
2.1.2. Platform-as-a-Service (PaaS)
2.1.3. Infrastructure-as-a-Service (IaaS)
2.2. Cloud Business Models Revisited
2.3. Cloud Deployment Models
2.4. Advantages and Drawbacks of Cloud Computing
3. Cloud Computing in Business
3.1. Cloud Computing: Economics
3.2. Cloud: Social Implications
3.3. Cloud Computing: Energy Efficiency
4. Cloud Applications in Business
4.4. Hospitality and Tourism
4.5. Music and Broadcasting Media
4.8. Manufacturing and Production
Chapter 3: Consulting as a Service - Demonstrated by Cloud Computing Consultancy Projects in Greater China
2. Consulting As a Service
2.1. Cloud Computing Adoption Framework Overview
2.2. Contributions from Taiwanese Research and Development in the Development of High-Technology Sector in China
3. Cloud Computing Development in
China – Shanghai Stock Exchange (SSE)
3.1. The Involvement with Teradata
3.2. Technology behind SSE and Teradata
5.1. Satellite Orbiting Saturn
5.2. Simulations of the Galaxy Formation
5.3. Simulations of the Galaxy Explosion
6.1. The Role of Cloud Computing Adoption Framework (CCAF)
6.2. The Added Values from These Three Projects As a Result of Taiwanese Contributions
6.3. Consulting As a Service (CaaS) – Successful Lessons to Be Reproduced in European Projects
Conclusion and Future Work
Chapter 4: Continuous Delivery in the Cloud: An Economic Evaluation Using System Dynamics
What is Continuous Delivery?
Dynamic Continuous Delivery
Continuous Delivery (CD) Meets the Cloud
Cloud Development Platform
Cloud Source Control System
Cloud Continuous Integration Server
Cloud Automated Acceptance Testing
System Dynamics Modelling of the Profitability of Cloud Based Continuous Delivery
Model Variables and Parameters
On-Site Deployment Variables
Total Initial Investment On-Premise Deployment
Cloud Deployment Variables
Chapter 5: Financial Clouds and Modelling Offered by Cloud Computing Adoption Framework
2.1. Organisational Challenges of Cloud Adoption
2.3.1. Monte Carlo Methods in Theory
2.3.2. Monte Carlo Methods for Variance-Gamma Processes
2.3. Black Scholes Model (BSM)
2. Motivation for the Cloud Computing Adoption Framework (CCAF)
3.1. Our Work for Research Questions within the CCAF
3.2. The Updated CCAF Architecture
3.3. The CCAF: Portability for Financial Software as a Service (FSaaS)
4. FSaaS Portability with Monte Carlo Methods (MCM) and Black Scholes Model (BSM)
4.1. Selection of MATLAB with Emphasis on Error Corrections
4.2. Monte Carlo in MATLAB – Calculating the Best Buy/Sell Prices
4.3. Coding Algorithm for Variance-Gamma Processes
4.4. The Outcome of Executing Variance-Gamma Processes
4.5. Experiment and Benchmark in the Cloud Environments
4.6. The Benchmark Results
4.7. Black Scholes Model (BSM) Coding Algorithm
4.7 Calculate call option price using explicit Finite Difference Scheme
4.8. Asset Steps Benchmark on the Clouds
5.1. Variance in Volatility, Maturity and Risk Free Rate
5.3. Implication for Banking
5.4. A Conceptual Financial Cloud Platform
5.5. Enterprise Portability to the Clouds
5.6. Variance-Gamma Processes (VPG) versus Least Square Methods (LSM)
Conclusion and Future Work
Chapter 6: Review of Cloud Computing and Existing Frameworks for Cloud Adoption
2. What Drives Organisations Adopting Cloud Computing?
2.1. Benefits and Characteristics of Cloud Computing Adoption
2.2. Surveys for Cloud Computing Adoption
2.3. Personalisation for Cloud Computing
3. Technical Review for Cloud Computing
3.1. Security for Cloud Computing
3.2. Portability for Cloud Computing
3.3. Business Integration
4. Cloud Computing for Business Use
5. Stakeholders’ Points of View: Risks for Organizational Adoption and How Risks are Related to Cloud Adoption Challenges
5.1. How Those Risks Relate to Cloud Adoption Challenges
5.2. Additional Cloud Adoption Challenges
6. A need for a Framework for Cloud Computing
7. Identified Problems with Existing Frameworks
7.1. Cloud Business Model Framework (CBMF)
7.2. Linthicum Cloud Computing Framework (LCCF)
7.3. Return on Investment (ROI) for Cloud Computing
7.4. Performance Metrics Framework
7.5. Oracle Consulting Cloud Computing Services Framework
7.6. IBM Framework for Cloud Adoption (IFCA)
7.8. BlueSky Cloud Framework for e-Learning
7.9. The Hybrid ITIL V3 Framework for Cloud
7.10. DAvinCi: A Cloud Computing Framework for Service Robots
7.11. Cloud Computing Business Framework (CCBF)
7.12. Summary of the Section
8.1. Desired Characteristics for a Proposed Framework
8.2. Future Challenges for Risk and Return Analysis
8.2.1. Costs (Financial) Measurement for Risk and Return Analysis
8.2.2. Technical Measurement for Risk and Return Analysis
8.2.3. Users (or Organisations) Measurement for Risk and Return Analysis
8.3. Future Directions Related to This Research
Part 2. Energy Efficient Cloud
Chapter 7: Estimating Emission Reductions from Low Carbon Information Technology: The GeoChronos Relocation Project
Project Type 1: Project Activities Involving Improvements to ICT Facilities
Project Type 2: Project Activities Involving Improvements to ICT Services
Description of Datacentres Involved in Project
Identification of SSRs Attributable to the Project
Selection and Justification of the Baseline Scenario
Type 2: Project activities involving improvements to ICT services
Identification of SSRs Attributable to The Baseline
Selection of Relevant SSRs for Quantification
or Estimation of GHG Emission Reductions
Method to Quantify/Estimate GHG Emissions
and/or Removals in the Baseline and Project
Determining Emissions in the Baseline Scenario
Determining the PUE of the Baseline Facility
Determining the Weighted Emission Factor of Source Energy
Determining Emissions for the Project
Determining the Project ICT Power Usage
Determining the PUE of the Project Facility
Network Traffic Emissions Source
Quantification of GHG Emission Reductions and Removals
Quantifying Emissions for the Baseline
Quantifying Emissions for the Project
Estimated Emission Reductions
Conclusion and Discussion of Relevance
for Carbon Trading and Corporate
Chapter 8: Energy Efficiency of the Cloud Computing System
Cloud Computing Technology
Cloud Computing Adoption Rate
Components of the Cloud Computing System
The Energy Efficiency of Cloud Computing
Importance of Energy Efficient Improvements in the Cloud Computing System
Simulations on Data Centre Energy Consumption
Simulations Results Analysis and Discussion
Response Time and Energy Consumption
CPU Utilization and Energy Consumption
Comparing the Energy Consumption of the Cloud Computing Systems
1. Standard Cloud Computing System Energy Consumption
Communication Networks Component
Data Centre Network Component
Total Power Consumption of Standard Cloud Computing System
Energy Consumption in 24 Hours Using Formula 2
2. Green Cloud Computing System
Communications Network Component
3. Total Power Consumption of Green Cloud Computing System
Energy Consumption in 24 Hours Using Formula 2
Conclusion and Future Work
Advances in Cloud Computing Research
( Computer Science, Technology and Applications )
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