| 紀錄類型: |
書目-電子資源
: Monograph/item
|
| 正題名/作者: |
Software architecture for big data and the cloud/ edited by Ivan Mistrik [and 4 others]. |
| 其他作者: |
Mistrík, Ivan, |
| 出版者: |
Cambridge, MA :Morgan Kaufmann, : 2017., |
| 面頁冊數: |
1 online resource (xxxviii, 432 p.) |
| 內容註: |
Machine generated contents note: ch. 1 Introduction. Software Architecture for Cloud and Big Data: An Open Quest for the Architecturally Significant Requirements / Ivan Mistrik -- 1.1.A Perspective into Software Architecture for Cloud and Big Data -- 1.2.Cloud Architecturally Significant Requirements and Their Design Implications -- 1.2.1.Dynamism and Elasticity as Cloud Architecturally Significant Requirements -- 1.2.2.Multitenancy as Cloud Architecturally Significant Requirement -- 1.2.3.Service Level Agreements (SLAs) Constraints as Cloud Architecturally Significant Requirement -- 1.2.4.Cloud Marketplaces as Architecturally Significant Requirement -- 1.2.5.Seeking Value as Cloud Architecturally Significant Requirement -- 1.3.Big Data Management as Cloud Architecturally Significant Requirement -- 1.3.1.Big Data Analytics Enabled by the Cloud and Its Architecturally Significant Requirements |
| 內容註: |
Note continued: 1.3.2.Architecturally Significant Requirements in Realm of Competing Big Data Technologies -- References -- pt. 1 CONCEPTS AND MODELS -- ch. 2 Hyperscalability -- The Changing Face of Software Architecture / Ian Gorton -- 2.1.Introduction -- 2.2.Hyperscalable Systems -- 2.2.1.Scalability -- 2.2.2.Scalability Limits -- 2.2.3.Scalability Costs -- 2.2.4.Hyperscalability -- 2.3.Principles of Hyperscalable Systems -- 2.3.1.Automate and Optimize to Control Costs -- 2.3.2.Simple Solutions Promote Scalability -- 2.3.3.Utilize Stateless Services -- 2.3.4.Observability is Fundamental to Success at Hyperscale -- 2.4.Related Work -- 2.5.Conclusions -- References -- ch. 3 Architecting to Deliver Value From a Big Data and Hybrid Cloud Architecture / Tim Vincent -- 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 |
| 內容註: |
Note continued: 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 -- ch. 4 Domain-Driven Design of Big Data Systems Based on a Reference Architecture / Ioannis N. Athanasiadis -- 4.1.Introduction -- 4.2.Domain-Driven Design Approach -- 4.3.Related Work -- 4.4.Feature Model of Big Data Systems -- 4.4.1.Data -- 4.4.2.Information Management -- 4.4.3.Interface and Visualization -- 4.4.4.Data Processing -- 4.4.5.Data Storage -- 4.4.6.Data Analysis -- 4.4.7.Feature Constraints -- 4.5.Deriving the Application Architectures and Example -- 4.5.1.Feature Modeling |
| 內容註: |
Note continued: 4.5.2.Design Rule Modeling -- 4.5.3.Associating Design Decisions With Features -- 4.5.4.Generation of the Application Architecture and the Deployment Diagram -- 4.5.5.Deriving Big Data Architectures of Existing Systems -- 4.6.Conclusion -- References -- ch. 5 An Architectural Model-Based Approach to Quality-Aware DevOps in Cloud Applications / Ralf Reussner -- 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 -- 5.5.1.The iObserve Megamodel -- 5.5.2.Descriptive and Prescriptive Architectural Models in iObserve -- 5.5.3.Static and Dynamic Content in Architectural Models -- 5.6.Applying iObserve to CoCoME -- 5.6.1.Applying the iObserve Megamodel -- 5.6.2.Applying Descriptive and Prescriptive Architectural Models -- 5.6.3.Applying Live Visualization -- 5.7.Limitations |
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Note continued: 5.8.Related Work -- 5.9.Conclusion -- References -- ch. 6 Bridging Ecology and Cloud: Transposing Ecological Perspective to Enable Better Cloud Autoscaling / Rami Bahsoon -- 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 -- Acknowledgement -- pt. 2 ANALYZING AND EVALUATING -- ch. 7 Evaluating Web PKIs / Mark Ryan -- 7.1.Introduction -- 7.2.An Overview of PKI -- 7.3.Desired Features and Security Concerns -- 7.4.Existing Proposals -- 7.4.1.Classic -- 7.4.2.Difference Observation -- 7.4.3.Scope Restriction -- 7.4.4.Certificate Management Transparency -- 7.5.Observations -- 7.5.1.Property Perspective -- 7.5.2.System Perspective -- 7.6.Conclusion -- References |
| 內容註: |
Note continued: ch. 8 Performance Isolation in Cloud-Based Big Data Architectures / Alp Oral -- 8.1.Introduction -- 8.2.Background -- 8.2.1.Cloud Computing -- 8.2.2.Big Data Architecture -- 8.3.Case Study and Problem Statement -- 8.3.1.Case Study -- 8.3.2.Problem Statement -- 8.4.Performance Monitoring in Cloud-Based Systems -- 8.5.Application Framework for Performance Isolation -- 8.6.Evaluation of the Framework -- 8.6.1.Evaluation Results -- 8.7.Discussion -- 8.8.Related Work -- 8.9.Conclusion -- References -- ch. 9 From Legacy to Cloud: Risks and Benefits in Software Cloud Migration / Patricia Lago -- 9.1.Introduction -- 9.2.Research Method -- 9.2.1.Pilot Study -- 9.2.2.Search Strategy -- 9.2.3.Data Extraction -- 9.2.4.Data Analysis Method -- 9.3.Results -- 9.3.1.Overview of Primary Studies and Quality Evaluation -- 9.3.2.Benefits and Risks -- 9.3.3.General Measures -- 9.3.4.Models and Frameworks for Cloud Migration -- 9.4.Discussion |
| 內容註: |
Note continued: 9.4.1.Findings and Lessons Learned -- 9.4.2.Threats to Validity -- 9.5.Conclusion -- References -- ch. 10 Big Data: A Practitioners Perspective / Fiona O'Sullivan -- 10.1.Big Data Is a New Paradigm -- Differences With Traditional Data Warehouse, Pitfalls and Consideration -- 10.1.1.Differences With Traditional Data Warehouse -- 10.1.2.Pitfalls -- 10.1.3.Considerations -- 10.2.Product Considerations for Big Data -- Use of Open Source Products for Big Data, Pitfalls and Considerations -- 10.2.1.The Use of Open Source Product for Big Data -- 10.2.2.Pitfalls -- 10.2.3.Considerations -- 10.3.Use of Cloud for hosting Big Data -- Why to Use Cloud, Pitfalls and Consideration -- 10.3.1.Why to UseCloud? -- 10.3.2.Pitfalls -- 10.3.3.Consideration -- 10.4.Big Data Implementation -- Architecture Definition, Processing Framework and Migration Pattern From Data Warehouse to Big Data -- 10.4.1.Patterns for Transitioning From Data Warehouse to Big Data |
| 內容註: |
Note continued: 10.5.Conclusion -- References -- pt. 3 TECHNOLOGIES -- ch. 11 A Taxonomy and Survey of Stream Processing Systems / Rajkumar Buyya -- 11.1.Introduction -- 11.2.Stream Processing Platforms: A Brief Background -- 11.2.1.Requirements of Stream Processing Platforms/Engines -- 11.2.2.Generic Model of Modern Stream Processing Platforms/Engines -- 11.3.Taxonomy -- 11.3.1.Functional Aspects -- 11.3.2.Nonfunctional Aspects -- 11.4.A Survey of Stream Processing Platforms -- 11.4.1.Data Stream Management Systems -- 11.4.2.Complex Event Processing Systems -- 11.4.3.Stream Processing Platforms/Engines -- 11.5.Comparison Study of the Stream Processing Platforms -- 11.5.1.Scalability -- 11.5.2.Messaging& Distribution -- 11.5.3.Data Processing/Stream Processors -- 11.5.4.Fault Tolerance -- 11.6.Conclusions and Future Directions -- References -- ch. 12 Architecting Cloud Services for the Digital Me in a Privacy-Aware Environment / Andreas Wortmann -- 12.1.Introduction |
| 內容註: |
Note continued: 12.2.Example -- 12.3.Challenges -- 12.3.1.Service Composition -- 12.3.2.Technology Abstraction -- 12.3.3.Service and Data Integration -- 12.3.4.Trusted Use of Personal Data -- 12.4.Preliminaries -- 12.5.System-of-Systems Approach -- 12.5.1.Persistence Service -- 12.5.2.DataConversion Service -- 12.5.3.Privacy Service -- 12.5.4.LookUp Service -- 12.5.5.PersonalData Service -- 12.6.Generative Approach -- 12.7.Related Work -- 12.7.1.Service Composition --12.7.2.Technology Abstraction -- 12.7.3.Service and Data Integration -- 12.7.4.Trusted Use of Personal Data -- 12.8.Discussion -- 12.9.Conclusion -- References -- ch. 13 Reengineering Data-Centric Information Systems for the Cloud -- A Method and Architectural Patterns Promoting Multitenancy / Olaf Zimmermann -- 13.1.Introduction -- 13.2.Context and Problem: Multitenancy in Cloud Computing -- 13.3.Solution Overview: Reengineering Method and Process |
| 內容註: |
Note continued: 13.4.Solution Detail 1: Architectural Patterns in the Method -- 13.4.1.Architectural Reengineering Steps for the Cloud (Architectural Refactoring) -- 13.4.2.Multitenancy Requirements and Patterns for Cloud Environments -- 13.4.3.The Multitenancy Capable Model -- 13.4.4.The Multitenancy Capable Controller -- 13.4.5.The Multitenancy Capable View -- 13.5.Solution Detail 2: Testing and Code Reviews -- 13.5.1.Testing for Multitenancy Defects -- 13.5.2.Code Review for Multitenancy Defects -- 13.5.3.Summary -- 13.6.Case Study (Implementation) -- 13.6.1.Multitenancy Transformation Without Patterns -- 13.6.2.Multitenancy Transformation With Patterns -- 13.6.3.Comparison -- 13.7.Discussion -- 13.8.Related Work -- 13.9.Summary and Conclusions -- Appendix 13.A Architectural Refactoring (AR) Reference -- References -- ch. 14 Exploring the Evolution of Big Data Technologies / Georgios Theodoropoulos -- 14.1.Introduction -- 14.2.Big Datain Our Daily Lives |
| 內容註: |
Note continued: 14.3.Data Intensive Computing -- 14.3.1.Big Compute Versus Big Data -- 14.3.2.Data Intensive Applications -- 14.3.3.Data Intensive Frameworks -- 14.3.4.MapReduce and GFS -- 14.4.Apache Hadoop -- 14.4.1.Hadoop VI -- 14.4.2.Hadoop 2.0 -- 14.5.Apache Spark -- 14.5.1.Resilient Distributed Datasets -- 14.5.2.Data Flow and Programming With Spark -- 14.5.3.Spark Processing Engines -- 14.5.4.Hadoop Ecosystem Taxonomy -- 14.6.The Role of Cloud Computing -- 14.7.The Future of Big Data Platforms -- 14.7.1.Big Data Applications -- 14.7.2.Big Data Frameworks and Hardware -- 14.7.3.Big Data on the Road to Exascale -- 14.8.Conclusion -- References -- ch. 15 A Taxonomy and Survey of Fault-Tolerant Workflow Management Systems in Cloud and Distributed Computing Environments / Rajkumar Buyya -- 15.1.Introduction -- 15.2.Background -- 15.2.1.Workflow Management Systems -- 15.2.2.Workflow Scheduling -- 15.3.Introduction to Fault-Tolerance |
| 內容註: |
Note continued: 15.3.1.Necessity for Fault-Tolerance in Distributed Systems -- 15.4.Taxonomy of Faults -- 15.5.Taxonomy of Fault-Tolerant Scheduling Algorithms -- 15.5.1.Replication -- 15.5.2.Resubmission -- 15.5.3.Checkpointing -- 15.5.4.Provenance -- 15.5.5.Rescue Workflow -- 15.5.6.User-Defined Exception Handling -- 15.5.7.Alternate Task -- 15.5.8.Failure Masking -- 15.5.9.Slack Time -- 15.5.10.Trust-Based 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 -- 15.8.1.Askalon -- 15.8.2.Pegasus -- 15.8.3.Triana -- 15.8.4.UNICORE 6 -- 15.8.5.Kepler -- 15.8.6.Cloudbus Workflow Management System -- 15.8.7.Traverna -- 15.8.8.The e-Science Central (e-SC) -- 15.8.9.SwinDeW-C -- 15.8.10.Big Data Workflow Frameworks: MapReduce, Hadoop, and Spark -- 15.8.11.Other Workflow Management Systems -- 15.9.Tools and Support Systems |
| 內容註: |
Note continued: 15.9.1.Data Management Tools -- 15.9.2.Security and Fault-Tolerance Management Tools -- 15.9.3.Cloud Development Tools -- 15.9.4.Support Systems -- 15.10.Summary -- References -- pt. 4 RESOURCE MANAGEMENT -- ch. 16 The HARNESS Platform: A Hardware- and Network-Enhanced Software System for Cloud Computing / Alexander Wolf -- 16.1.Introduction -- 16.2.Related Work -- 16.3.Overview -- 16.4.Managing Heterogeneity -- 16.4.1.Hierarchical Resource Management -- 16.4.2.Agnostic Resource Management -- 16.4.3.Ranking Allocation Requests -- 16.4.4.HARNESS API -- 16.5.Prototype Description -- 16.5.1.The Platform Layer -- 16.5.2.The Infrastructure Layer -- 16.5.3.The Virtual Execution Layer -- 16.6.Evaluation -- 16.6.1.Executing HPC Applications on the Cloud -- 16.6.2.Resource Scheduling with Network Constraints -- 16.7.Conclusion -- Project Resources -- References -- Acknowledgements -- ch. 17 Auditable Version Control Systems in Untrusted Public Clouds / Jun Dai |
| 內容註: |
Note continued: 17.1.Motivation and Contributions -- 17.2.Background Knowledge -- 17.2.1.Data Organization in Version Control Systems -- 17.2.2.Remote Data Integrity Checking (RDIC) -- 17.3.System and Adversarial Model -- 17.4.Auditable Version Control Systems -- 17.4.1.Definition of AVCS -- 17.4.2.An AVCS Construction -- 17.5.Discussion -- 17.6.Other RDIC Approaches for Version Control Systems -- 17.7.Evaluation -- 17.7.1.Theoretical Evaluation -- 17.7.2.ExperimentalEvaluation -- 17.8.Conclusion -- References -- ch. 18 Scientific Workflow Management System for Clouds / Rajkumar Buyya -- 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 -- 18.6.1.WRPS -- 18.6.2.Montage -- 18.6.3.Setup of Experimental Infrastructure -- 18.6.4.Montage Setup -- 18.6.5.Results -- 18.7.Summary and Conclusions -- References |
| 內容註: |
Note continued: pt. 5 LOOKING AHEAD -- ch. 19 Outlook and Future Directions / Ivan Mistrik -- 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. |
| 標題: |
Software engineering. - |
| 電子資源: |
https://www.sciencedirect.com/science/book/9780128054673 |
| ISBN: |
9780128093382 (electronic bk.) |