Model-Driven Development and Operation of Multi-Cloud Applications : The MODAClouds Approach.

By: Di Nitto, ElisabettaContributor(s): Matthews, Peter | Petcu, Dana | Solberg, ArnorMaterial type: TextTextSeries: SpringerBriefs in Applied Sciences and Technology SeriesPublisher: Cham : Springer International Publishing AG, 2017Copyright date: �2017Edition: 1st edDescription: 1 online resource (154 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9783319460314Genre/Form: Electronic books.Additional physical formats: Print version:: Model-Driven Development and Operation of Multi-Cloud ApplicationsLOC classification: TK5101-5105.9Online resources: Click to View
Contents:
Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Context -- 1.2 Motivation -- 1.3 Related Work -- 1.4 The MODAClouds Approach -- 1.5 The MODAClouds Toolbox -- 1.6 Book Objectives -- References -- 2 Cloud Service Offer Selection -- 2.1 Introduction: Selecting Services for Agile Application Development -- 2.2 Decision Support System for Cloud Service Selection -- 2.3 Cloud Service Description Standardization -- 2.4 Data Gathering in Multi-Cloud Environments -- 2.5 Coping with Complexity in SaaS -- 2.6 Decision Support Tools for Cloud Service Selection -- 2.7 Technical Challenges and Implementation -- 2.8 Conclusion: Evolution of Cloud Services, Decision Support and Future Work -- Reference -- 3 The MODAClouds Model-Driven Development -- 3.1 Introduction -- 3.2 The Design-Time Development Process -- 3.3 Overall Language Architecture -- 3.4 MODACloudML Sub Models -- 3.4.1 CCIM Models -- 3.4.2 Example -- 3.4.3 CPIM and CPSM Models -- 3.4.4 Example -- 3.5 Related Work -- 3.6 Conclusion -- References -- 4 QoS Assessment and SLA Management -- 4.1 Introduction -- 4.2 Case Study: Meeting in the Cloud (MiC) -- 4.3 QoS Assessment and Optimisation -- 4.3.1 Assessment -- 4.3.2 Optimisation -- 4.3.3 LINE -- 4.4 SLA Management -- References -- 5 Monitoring in a Multi-cloud Environment -- 5.1 Introduction -- 5.2 Tower 4Clouds Architecture -- 5.3 Application Configuration Model -- 5.4 Monitoring Rules -- 5.5 Conclusion -- References -- 6 Load Balancing for Multi-cloud -- 6.1 Introduction -- 6.2 Load Balancing Controller -- 6.3 Load Balancing Reasoner -- 6.4 Multi-cloud Load Balancing -- 6.4.1 Usage Scenario of Multi-cloud Load Balancing -- 6.5 Load Balancing and Failure Management -- 6.6 Conclusion -- References -- 7 Fault-Tolerant Off-line Data Migration: The Hegira4Clouds Approach -- 7.1 Introduction -- 7.2 Hegira4Clouds Intermediate Meta-Model.
7.3 Architecture and Fault Tolerance Features -- 7.3.1 Virtual Data Partitioning -- 7.3.2 Recovering from Faults -- 7.4 Evaluation: Migrating Tweets -- 7.5 Discussion and Conclusion -- References -- 8 Deployment of Cloud Supporting Services -- 8.1 Introduction -- 8.2 MODAClouds Execution Platform -- 8.2.1 mOS -- 8.2.2 Platform Sub-systems -- 8.3 Supporting Services -- 8.3.1 Object Store -- 8.3.2 Artifact Repository -- 8.3.3 Load Balancer Controller -- 8.3.4 Batch Engine -- 8.4 Conclusions -- References -- 9 Models@Runtime for Continuous Design and Deployment -- 9.1 Introduction -- 9.2 The Models@Runtime Approach -- 9.3 The MODAClouds Models@Runtime Engine -- 9.3.1 The Comparison Engine -- 9.3.2 Adaptation Commands -- 9.3.3 State Tracking -- 9.3.4 Interaction with the Models@Runtime Engine -- 9.4 Related Work -- 9.5 Conclusion -- References -- 10 Closing the Loop Between Ops and Dev -- 10.1 Introduction -- 10.2 FG Architecture -- 10.2.1 FG Analyzer -- 10.2.2 FG Actuator -- 10.2.3 FG Reporter -- 10.3 Workflow -- 10.4 Estimation Techniques for FG Analysis -- 10.4.1 A Bayesian Approach Based on Queue-Lengths -- 10.4.2 A Maximum-Likelihood Approach Based on Queue-Lengths and Response Times -- 10.5 Conclusion -- References -- 11 Cloud Patterns -- 11.1 Introduction -- 11.2 Motivational Guidance -- 11.3 MODAClouds-Specific Patterns -- 11.4 Conclusions -- References -- 12 Modelio Project Management Server Constellation -- 12.1 Introduction -- 12.2 Proposed Architecture -- 12.3 Use of MODAClouds Design and Runtime Components -- 12.3.1 Modelling with Creator 4Clouds -- 12.3.2 Multi-cloud Deployment with CloudML 4Clouds -- 12.3.3 Cost and Performance Analysis with SPACE 4Clouds -- 12.3.4 Multi-cloud Monitoring and Management with Energizer 4Clouds -- 12.4 Conclusion -- References -- 13 BPM in the Cloud: The BOC Case -- 13.1 Introduction.
13.2 Context and Motivation -- 13.3 Application Scenario -- 13.3.1 Cloud Provider Selection -- 13.3.2 Application Deployment to Multiple Clouds -- 13.3.3 Cloud Application Monitoring -- 13.3.4 Cloud to Cloud Migration -- 13.4 Conclusion and General Recommendations -- References -- 14 Healthcare Application -- 14.1 Introduction -- 14.2 EHealth Cloud Solution: Why to Deploy It in a Multi-Cloud Environment? -- 14.3 Risks and Problems -- 14.4 EHealth and MODAClouds: The Story -- 14.5 Conclusions -- References -- 15 Operation Control Interfaces -- 15.1 Introduction -- 15.2 Language for Triggers Description -- 15.3 Architecture of the Trigger Support -- 15.4 Usage of Triggers to Enable Load Balancing -- 15.5 Related Work -- 15.6 Conclusions -- References -- 16 Conclusion and Future Research -- 16.1 Summary -- 16.2 Outlook and Further Research.
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Intro -- Preface -- Contents -- 1 Introduction -- 1.1 Context -- 1.2 Motivation -- 1.3 Related Work -- 1.4 The MODAClouds Approach -- 1.5 The MODAClouds Toolbox -- 1.6 Book Objectives -- References -- 2 Cloud Service Offer Selection -- 2.1 Introduction: Selecting Services for Agile Application Development -- 2.2 Decision Support System for Cloud Service Selection -- 2.3 Cloud Service Description Standardization -- 2.4 Data Gathering in Multi-Cloud Environments -- 2.5 Coping with Complexity in SaaS -- 2.6 Decision Support Tools for Cloud Service Selection -- 2.7 Technical Challenges and Implementation -- 2.8 Conclusion: Evolution of Cloud Services, Decision Support and Future Work -- Reference -- 3 The MODAClouds Model-Driven Development -- 3.1 Introduction -- 3.2 The Design-Time Development Process -- 3.3 Overall Language Architecture -- 3.4 MODACloudML Sub Models -- 3.4.1 CCIM Models -- 3.4.2 Example -- 3.4.3 CPIM and CPSM Models -- 3.4.4 Example -- 3.5 Related Work -- 3.6 Conclusion -- References -- 4 QoS Assessment and SLA Management -- 4.1 Introduction -- 4.2 Case Study: Meeting in the Cloud (MiC) -- 4.3 QoS Assessment and Optimisation -- 4.3.1 Assessment -- 4.3.2 Optimisation -- 4.3.3 LINE -- 4.4 SLA Management -- References -- 5 Monitoring in a Multi-cloud Environment -- 5.1 Introduction -- 5.2 Tower 4Clouds Architecture -- 5.3 Application Configuration Model -- 5.4 Monitoring Rules -- 5.5 Conclusion -- References -- 6 Load Balancing for Multi-cloud -- 6.1 Introduction -- 6.2 Load Balancing Controller -- 6.3 Load Balancing Reasoner -- 6.4 Multi-cloud Load Balancing -- 6.4.1 Usage Scenario of Multi-cloud Load Balancing -- 6.5 Load Balancing and Failure Management -- 6.6 Conclusion -- References -- 7 Fault-Tolerant Off-line Data Migration: The Hegira4Clouds Approach -- 7.1 Introduction -- 7.2 Hegira4Clouds Intermediate Meta-Model.

7.3 Architecture and Fault Tolerance Features -- 7.3.1 Virtual Data Partitioning -- 7.3.2 Recovering from Faults -- 7.4 Evaluation: Migrating Tweets -- 7.5 Discussion and Conclusion -- References -- 8 Deployment of Cloud Supporting Services -- 8.1 Introduction -- 8.2 MODAClouds Execution Platform -- 8.2.1 mOS -- 8.2.2 Platform Sub-systems -- 8.3 Supporting Services -- 8.3.1 Object Store -- 8.3.2 Artifact Repository -- 8.3.3 Load Balancer Controller -- 8.3.4 Batch Engine -- 8.4 Conclusions -- References -- 9 Models@Runtime for Continuous Design and Deployment -- 9.1 Introduction -- 9.2 The Models@Runtime Approach -- 9.3 The MODAClouds Models@Runtime Engine -- 9.3.1 The Comparison Engine -- 9.3.2 Adaptation Commands -- 9.3.3 State Tracking -- 9.3.4 Interaction with the Models@Runtime Engine -- 9.4 Related Work -- 9.5 Conclusion -- References -- 10 Closing the Loop Between Ops and Dev -- 10.1 Introduction -- 10.2 FG Architecture -- 10.2.1 FG Analyzer -- 10.2.2 FG Actuator -- 10.2.3 FG Reporter -- 10.3 Workflow -- 10.4 Estimation Techniques for FG Analysis -- 10.4.1 A Bayesian Approach Based on Queue-Lengths -- 10.4.2 A Maximum-Likelihood Approach Based on Queue-Lengths and Response Times -- 10.5 Conclusion -- References -- 11 Cloud Patterns -- 11.1 Introduction -- 11.2 Motivational Guidance -- 11.3 MODAClouds-Specific Patterns -- 11.4 Conclusions -- References -- 12 Modelio Project Management Server Constellation -- 12.1 Introduction -- 12.2 Proposed Architecture -- 12.3 Use of MODAClouds Design and Runtime Components -- 12.3.1 Modelling with Creator 4Clouds -- 12.3.2 Multi-cloud Deployment with CloudML 4Clouds -- 12.3.3 Cost and Performance Analysis with SPACE 4Clouds -- 12.3.4 Multi-cloud Monitoring and Management with Energizer 4Clouds -- 12.4 Conclusion -- References -- 13 BPM in the Cloud: The BOC Case -- 13.1 Introduction.

13.2 Context and Motivation -- 13.3 Application Scenario -- 13.3.1 Cloud Provider Selection -- 13.3.2 Application Deployment to Multiple Clouds -- 13.3.3 Cloud Application Monitoring -- 13.3.4 Cloud to Cloud Migration -- 13.4 Conclusion and General Recommendations -- References -- 14 Healthcare Application -- 14.1 Introduction -- 14.2 EHealth Cloud Solution: Why to Deploy It in a Multi-Cloud Environment? -- 14.3 Risks and Problems -- 14.4 EHealth and MODAClouds: The Story -- 14.5 Conclusions -- References -- 15 Operation Control Interfaces -- 15.1 Introduction -- 15.2 Language for Triggers Description -- 15.3 Architecture of the Trigger Support -- 15.4 Usage of Triggers to Enable Load Balancing -- 15.5 Related Work -- 15.6 Conclusions -- References -- 16 Conclusion and Future Research -- 16.1 Summary -- 16.2 Outlook and Further Research.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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