Software Architecture for Big Data and the Cloud

830 Pages·2017·27.97 MB·English

by  Ivan Mistrik

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
2. 11.1. Introduction
3. 11.2. Stream Processing Platforms: A Brief Background
4. 11.3. Taxonomy
5. 11.4. A Survey of Stream Processing Platforms
6. 11.5. Comparison Study of the Stream Processing Platforms
7. 11.6. Conclusions and Future Directions
8. References
2. Chapter 12: Architecting Cloud Services for the Digital Me in a
Privacy-Aware Environment
1. Abstract
2. 12.1. Introduction
3. 12.2. Example
4. 12.3. Challenges
5. 12.4. Preliminaries
6. 12.5. System-of-Systems Approach
7. 12.6. Generative Approach
8. 12.7. Related Work
9. 12.8. Discussion
10. 12.9. Conclusion
11. References
3. Chapter 13: Reengineering Data-Centric Information Systems for the
Cloud – A Method and Architectural Patterns Promoting Multitenancy
1. Abstract
2. 13.1. Introduction
3. 13.2. Context and Problem: Multitenancy in Cloud Computing
4. 13.3. Solution Overview: Reengineering Method and Process
5. 13.4. Solution Detail 1: Architectural Patterns in the Method
6. 13.5. Solution Detail 2: Testing and Code Reviews
7. 13.6. Case Study (Implementation)
8. 13.7. Discussion
9. 13.8. Related Work
10. 13.9. Summary and Conclusions
11. Appendix 13.A. Architectural Refactoring (AR) Reference
12. References
4. Chapter 14: Exploring the Evolution of Big Data Technologies
1. Abstract
2. 14.1. Introduction
3. 14.2. Big Data in Our Daily Lives
4. 14.3. Data Intensive Computing
5. 14.4. Apache Hadoop
6. 14.5. Apache Spark
7. 14.6. The Role of Cloud Computing
8. 14.7. The Future of Big Data Platforms
9. 14.8. Conclusion
10. References
5. Chapter 15: A Taxonomy and Survey of Fault-Tolerant Workflow
Management Systems in Cloud and Distributed Computing
Environments
1. Abstract
2. 15.1. Introduction
3. 15.2. Background
4. 15.3. Introduction to Fault-Tolerance
5. 15.4. Taxonomy of Faults
6. 15.5. Taxonomy of Fault-Tolerant Scheduling Algorithms
7. 15.6. Modeling of Failures in Workflow Management Systems
8. 15.7. Metrics Used to Quantify Fault-Tolerance
9. 15.8. Survey of Workflow Management Systems and
Frameworks
10. 15.9. Tools and Support Systems
11. 15.10. Summary
12. References
14. Part 4: Resource Management
1. Chapter 16: The HARNESS Platform: A Hardware-and Network-
Enhanced Software System for Cloud Computing
1. Abstract
2. Acknowledgements
3. 16.1. Introduction
4. 16.2. Related Work
5. 16.3. Overview
6. 16.4. Managing Heterogeneity
7. 16.5. Prototype Description
8. 16.6. Evaluation
9. 16.7. Conclusion
10. Project Resources
11. References
2. Chapter 17: Auditable Version Control Systems in Untrusted Public
Clouds
1. Abstract
2. 17.1. Motivation and Contributions
3. 17.2. Background Knowledge
4. 17.3. System and Adversarial Model
5. 17.4. Auditable Version Control Systems
6. 17.5. Discussion
7. 17.6. Other RDIC Approaches for Version Control Systems
8. 17.7. Evaluation
9. 17.8. Conclusion
10. References
3. Chapter 18: Scientific Workflow Management System for Clouds
1. Abstract
2. 18.1. Introduction
3. 18.2. Background
4. 18.3. Workflow Management Systems for Clouds
5. 18.4. Cloudbus Workflow Management System
6. 18.5. Cloud-Based Extensions to the Workflow Engine
7. 18.6. Performance Evaluation
8. 18.7. Summary and Conclusions
9. References
15. Part 5: Looking Ahead
1. Chapter 19: Outlook and Future Directions
1. Abstract
2. 19.1. New or Advanced Applications
3. 19.2. Advanced Supporting Technologies
4. 19.3. Architecturally Significant Requirements
5. 19.4. Challenges for the Architecting Process
6. 19.5. Further Reading
7. References
16. Glossary
17. Author Index
18. Subject Index
List of tables
1. Tables in Chapter 4
1. Table 4.1
2. Table 4.2
2. Tables in Chapter 6
1. Table 6.1
3. Tables in Chapter 7
1. Table 7.1
2. Table 7.2
4. Tables in Chapter 8
1. Table 8.1
2. Table 8.2
3. Table 8.3
4. Table 8.4
5. Tables in Chapter 9
1. Table 9.1
2. Table 9.2
3. Table 9.3
4. Table 9.4
5. Table 9.5
6. Table 9.6
7. Table 9.7
8. Table 9.8
9. Table 9.9
6. Tables in Chapter 10
1. Table 10.1
7. Tables in Chapter 11
1. Table 11.1
2. Table 11.2
3. Table 11.3
4. Table 11.4
8. Tables in Chapter 13
1. Table 13.1
2. Table 13.2
3. Table 13.3
4. Table 13.4