Software Architecture for Big Data and the Cloud 1st edition by Morgan Kaufmann ISBN 0128093382 9780128093382

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ISBN 10: 0128093382
ISBN 13: 9780128093382
Author: Morgan Kaufmann

Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity.

The book’s editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors.

• Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques
• Presents case studies involving enterprise, business, and government service deployment of big data applications
• Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

Software Architecture for Big Data and the Cloud 1st Table of contents:

Part I: Concepts and Models

• Chapter 1: Introduction: Software Architecture for Cloud and Big Data: An Open Quest for the Architecturally Significant Requirements

  • Abstract

  • 1.1. A Perspective into Software Architecture for Cloud and Big Data

  • 1.2. Cloud Architecturally Significant Requirements and Their Design Implications

  • 1.3. Big Data Management as Cloud Architecturally Significant Requirement

  • References

• Chapter 2: Hyperscalability – The Changing Face of Software Architecture

  • Abstract

  • 2.1. Introduction

  • 2.2. Hyperscalable Systems

  • 2.3. Principles of Hyperscalable Systems

  • 2.4. Related Work

  • 2.5. Conclusions

  • References

• Chapter 3: Architecting to Deliver Value From a Big Data and Hybrid Cloud Architecture

  • Abstract

  • 3.1. Introduction

  • 3.2. Supporting the Analytics Lifecycle

  • 3.3. The Role of Data Lakes

  • 3.4. Key Design Features That Make a Data Lake Successful

  • 3.5. Architecture Example – Context Management in the IoT

  • 3.6. Big Data Origins and Characteristics

  • 3.7. The Systems That Capture and Process Big Data

  • 3.8. Operating Across Organizational Silos

  • 3.9. Architecture Example – Local Processing of Big Data

  • 3.10. Architecture Example – Creating a Multichannel View

  • 3.11. Application Independent Data

  • 3.12. Metadata and Governance

  • 3.13. Conclusions

  • 3.14. Outlook and Future Directions

  • References

• Chapter 4: Domain-Driven Design of Big Data Systems Based on a Reference Architecture

  • Abstract

  • 4.1. Introduction

  • 4.2. Domain-Driven Design Approach

  • 4.3. Related Work

  • 4.4. Feature Model of Big Data Systems

  • 4.5. Deriving the Application Architectures and Example

  • 4.6. Conclusion

  • References

• Chapter 5: An Architectural Model-Based Approach to Quality-Aware DevOps in Cloud Applications

  • Abstract

  • 5.1. Introduction

  • 5.2. A Cloud-Based Software Application

  • 5.3. Differences in Architectural Models Among Development and Operations

  • 5.4. The iObserve Approach

  • 5.5. Addressing the Differences in Architectural Models

  • 5.6. Applying iObserve to CoCoME

  • 5.7. Limitations

  • 5.8. Related Work

  • 5.9. Conclusion

  • References

• Chapter 6: Bridging Ecology and Cloud: Transposing Ecological Perspective to Enable Better Cloud Autoscaling

  • Acknowledgement

  • 6.1. Introduction

  • 6.2. Motivation

  • 6.3. Natural Ecosystem

  • 6.4. Transposing Ecological Principles, Theories and Models to Cloud Ecosystem

  • 6.5. Ecology-Inspired Self-Aware Pattern

  • 6.6. Opportunities and Challenges

  • 6.7. Related Work

  • 6.8. Conclusion

  • References

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Part II: Analyzing and Evaluating

• Chapter 7: Evaluating Web PKIs

  • Abstract

  • 7.1. Introduction

  • 7.2. An Overview of PKI

  • 7.3. Desired Features and Security Concerns

  • 7.4. Existing Proposals

  • 7.5. Observations

  • 7.6. Conclusion

  • References

• Chapter 8: Performance Isolation in Cloud-Based Big Data Architectures

  • Abstract

  • 8.1. Introduction

  • 8.2. Background

  • 8.3. Case Study and Problem Statement

  • 8.4. Performance Monitoring in Cloud-Based Systems

  • 8.5. Application Framework for Performance Isolation

  • 8.6. Evaluation of the Framework

  • 8.7. Discussion

  • 8.8. Related Work

  • 8.9. Conclusion

  • References

• Chapter 9: From Legacy to Cloud: Risks and Benefits in Software Cloud Migration

  • Abstract

  • 9.1. Introduction

  • 9.2. Research Method

  • 9.3. Results

  • 9.4. Discussion

  • 9.5. Conclusion

  • References

• Chapter 10: Big Data: A Practitioners Perspective

  • Abstract

  • 10.1. Big Data Is a New Paradigm – Differences With Traditional Data Warehouse, Pitfalls and Considerations

  • 10.2. Product Considerations for Big Data – Use of Open Source Products for Big Data, Pitfalls and Considerations

  • 10.3. Use of Cloud for Hosting Big Data – Why to Use Cloud, Pitfalls and Considerations

  • 10.4. Big Data Implementation – Architecture Definition, Processing Framework and Migration Pattern From Data Warehouse to Big Data

  • 10.5. Conclusion

  • References

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Part III: Technologies

• Chapter 11: A Taxonomy and Survey of Stream Processing Systems

  • Abstract

  • 11.1. Introduction

  • 11.2. Stream Processing Platforms: A Brief Background

  • 11.3. Taxonomy

  • 11.4. A Survey of Stream Processing Platforms

  • 11.5. Comparison Study of the Stream Processing Platforms

  • 11.6. Conclusions and Future Directions

  • References

• Chapter 12: Architecting Cloud Services for the Digital Me in a Privacy-Aware Environment

  • Abstract

  • 12.1. Introduction

  • 12.2. Example

  • 12.3. Challenges

  • 12.4. Preliminaries

  • 12.5. System-of-Systems Approach

  • 12.6. Generative Approach

  • 12.7. Related Work

  • 12.8. Discussion

  • 12.9. Conclusion

  • References

• Chapter 13: Reengineering Data-Centric Information Systems for the Cloud – A Method and Architectural Patterns Promoting Multitenancy

  • Abstract

  • 13.1. Introduction

  • 13.2. Context and Problem: Multitenancy in Cloud Computing

  • 13.3. Solution Overview: Reengineering Method and Process

  • 13.4. Solution Detail 1: Architectural Patterns in the Method

  • 13.5. Solution Detail 2: Testing and Code Reviews

  • 13.6. Case Study (Implementation)

  • 13.7. Discussion

  • 13.8. Related Work

  • 13.9. Summary and Conclusions

  • Appendix 13.A. Architectural Refactoring (AR) Reference

  • References

• Chapter 14: Exploring the Evolution of Big Data Technologies

  • Abstract

  • 14.1. Introduction

  • 14.2. Big Data in Our Daily Lives

  • 14.3. Data Intensive Computing

  • 14.4. Apache Hadoop

  • 14.5. Apache Spark

  • 14.6. The Role of Cloud Computing

  • 14.7. The Future of Big Data Platforms

  • 14.8. Conclusion

  • References

• Chapter 15: A Taxonomy and Survey of Fault-Tolerant Workflow Management Systems in Cloud and Distributed Computing Environments

  • Abstract

  • 15.1. Introduction

  • 15.2. Background

  • 15.3. Introduction to Fault-Tolerance

  • 15.4. Taxonomy of Faults

  • 15.5. Taxonomy of Fault-Tolerant Scheduling Algorithms

  • 15.6. Modeling of Failures in Workflow Management Systems

  • 15.7. Metrics Used to Quantify Fault-Tolerance

  • 15.8. Survey of Workflow Management Systems and Frameworks

  • 15.9. Tools and Support Systems

  • 15.10. Summary

  • References

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Part IV: Resource Management

• Chapter 16: The HARNESS Platform: A Hardware- and Network-Enhanced Software System for Cloud Computing

  • Abstract

  • Acknowledgements

  • 16.1. Introduction

  • 16.2. Related Work

  • 16.3. Overview

  • 16.4. Managing Heterogeneity

  • 16.5. Prototype Description

  • 16.6. Evaluation

  • 16.7. Conclusion

  • Project Resources

  • References

• Chapter 17: Auditable Version Control Systems in Untrusted Public Clouds

  • Abstract

  • 17.1. Motivation and Contributions

  • 17.2. Background Knowledge

  • 17.3. System and Adversarial Model

  • 17.4. Auditable Version Control Systems

  • 17.5. Discussion

  • 17.6. Other RDIC Approaches for Version Control Systems

  • 17.7. Evaluation

  • 17.8. Conclusion

  • References

• Chapter 18: Scientific Workflow Management System for Clouds

  • Abstract

  • 18.1. Introduction

  • 18.2. Background

  • 18.3. Workflow Management Systems for Clouds

  • 18.4. Cloudbus Workflow Management System

  • 18.5. Cloud-Based Extensions to the Workflow Engine

  • 18.6. Performance Evaluation

  • 18.7. Summary and Conclusions

  • References

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Part V: Looking Ahead

• Chapter 19: Outlook and Future Directions

  • Abstract

  • 19.1. New or Advanced Applications

  • 19.2. Advanced Supporting Technologies

  • 19.3. Architecturally Significant Requirements

  • 19.4. Challenges for the Architecting Process

  • 19.5. Further Reading

  • References

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Glossary
Author Index
Subject Index

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