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Software Architecture for Big Data and the Cloud 无水印pdf
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更新于2023-03-16
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Table of Contents
1. Cover image
2. Title page
3. Table of Contents
4. Copyright
5. Contributors
6. About the Editors
7. Foreword by Mandy Chessell
1. Amnesia or Progress?
8. Foreword by Ian Gorton
9. Preface
1. Introduction
2. Why a New Book on Software Architecture for Big Data and the
Cloud?
3. Book Outline
4. Part I: Concepts and Models
5. Part II: Analyzing and Evaluating
6. Part III: Technologies
7. Part IV: Resource Management
8. Part V: Looking Ahead
10. Chapter 1: Introduction. Software Architecture for Cloud and Big Data:
An Open Quest for the Architecturally Significant Requirements
1. Abstract
2. 1.1. A Perspective into Software Architecture for Cloud and Big
Data
3. 1.2. Cloud Architecturally Significant Requirements and Their
Design Implications
4. 1.3. Big Data Management as Cloud Architecturally Significant
Requirement
5. References
11. Part 1: Concepts and Models
1. Chapter 2: Hyperscalability – The Changing Face of Software
Architecture
1. Abstract
2. 2.1. Introduction
3. 2.2. Hyperscalable Systems
4. 2.3. Principles of Hyperscalable Systems
5. 2.4. Related Work
6. 2.5. Conclusions
7. References
2. Chapter 3: Architecting to Deliver Value From a Big Data and
Hybrid Cloud Architecture
1. Abstract
2. 3.1. Introduction
3. 3.2. Supporting the Analytics Lifecycle
4. 3.3. The Role of Data Lakes
5. 3.4. Key Design Features That Make a Data Lake Successful
6. 3.5. Architecture Example – Context Management in the IoT
7. 3.6. Big Data Origins and Characteristics
8. 3.7. The Systems That Capture and Process Big Data
9. 3.8. Operating Across Organizational Silos
10. 3.9. Architecture Example – Local Processing of Big Data
11. 3.10. Architecture Example – Creating a Multichannel View
12. 3.11. Application Independent Data
13. 3.12. Metadata and Governance
14. 3.13. Conclusions
15. 3.14. Outlook and Future Directions
16. References
3. Chapter 4: Domain-Driven Design of Big Data Systems Based on a
Reference Architecture
1. Abstract
2. 4.1. Introduction
3. 4.2. Domain-Driven Design Approach
4. 4.3. Related Work
5. 4.4. Feature Model of Big Data Systems
6. 4.5. Deriving the Application Architectures and Example
7. 4.6. Conclusion
8. References
4. Chapter 5: An Architectural Model-Based Approach to Quality-
Aware DevOps in Cloud Applicationsc
1. Abstract
2. 5.1. Introduction
3. 5.2. A Cloud-Based Software Application
4. 5.3. Differences in Architectural Models Among Development
and Operations
5. 5.4. The iObserve Approach
6. 5.5. Addressing the Differences in Architectural Models
7. 5.6. Applying iObserve to CoCoME
8. 5.7. Limitations
9. 5.8. Related Work
10. 5.9. Conclusion
11. References
5. Chapter 6: Bridging Ecology and Cloud: Transposing Ecological
Perspective to Enable Better Cloud Autoscaling
1. Abstract
2. Acknowledgement
3. 6.1. Introduction
4. 6.2. Motivation
5. 6.3. Natural Ecosystem
6. 6.4. Transposing Ecological Principles, Theories and Models
to Cloud Ecosystem
7. 6.5. Ecology-Inspired Self-Aware Pattern
8. 6.6. Opportunities and Challenges
9. 6.7. Related Work
10. 6.8. Conclusion
11. References
12. Part 2: Analyzing and Evaluating
1. Chapter 7: Evaluating Web PKIs
1. Abstract
2. 7.1. Introduction
3. 7.2. An Overview of PKI
4. 7.3. Desired Features and Security Concerns
5. 7.4. Existing Proposals
6. 7.5. Observations
7. 7.6. Conclusion
8. References
2. Chapter 8: Performance Isolation in Cloud-Based Big Data
Architectures
1. Abstract
2. 8.1. Introduction
3. 8.2. Background
4. 8.3. Case Study and Problem Statement
5. 8.4. Performance Monitoring in Cloud-Based Systems
6. 8.5. Application Framework for Performance Isolation
7. 8.6. Evaluation of the Framework
8. 8.7. Discussion
9. 8.8. Related Work
10. 8.9. Conclusion
11. References
3. Chapter 9: From Legacy to Cloud: Risks and Benefits in Software
Cloud Migration
1. Abstract
2. 9.1. Introduction
3. 9.2. Research Method
4. 9.3. Results
5. 9.4. Discussion
6. 9.5. Conclusion
7. References
4. Chapter 10: Big Data: A Practitioners Perspective
1. Abstract
2. 10.1. Big Data Is a New Paradigm – Differences With
Traditional Data Warehouse, Pitfalls and Consideration
3. 10.2. Product Considerations for Big Data – Use of Open
Source Products for Big Data, Pitfalls and Considerations
4. 10.3. Use of Cloud for hosting Big Data – Why to Use Cloud,
Pitfalls and Consideration
5. 10.4. Big Data Implementation – Architecture Definition,
Processing Framework and Migration Pattern From Data
Warehouse to Big Data
6. 10.5. Conclusion
7. References
13. Part 3: Technologies
1. Chapter 11: A Taxonomy and Survey of Stream Processing
Systems
1. Abstract
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