Title page; Table of Contents; Copyright; Contributors; About the Editors; Foreword by Mandy Chessell; Amnesia or Progress?; Foreword by Ian Gorton; Preface; Introduction; Why a New Book on Software Architecture for Big Data and the Cloud?; Book Outline; Part I: Concepts and Models; Part II: Analyzing and Evaluating; Part III: Technologies; Part IV: Resource Management; Part V: Looking Ahead; 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 Data1.2. Cloud Architecturally Significant Requirements and Their Design Implications; 1.3. Big Data Management as Cloud Architecturally Significant Requirement; References; Part 1: Concepts and Models; 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. Introduction3.2. 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 ArchitectureAbstract; 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 Applicationsc; 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 Models5.6. 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; Abstract; 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; Part 2: Analyzing and Evaluating.

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