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Table of Contents
Acknowledgements; Abstract; Contents; List of Abbreviations; Translation Types of Quality Parameters; List of Figures; List of Tables; Chapter 1: Business and IT Alignment: A Fuzzy Challenge; 1.1 Motivation of Research; 1.2 Research Issues and Thesis Structure; 1.3 Research Questions; 1.4 Research Methods; 1.5 General Information; References; Part I: SLA Dependency Mapping: Towards a Gradual and Bi-polar Concept; Chapter 2: The Complexity of Virtualized SLA Dependencies; 2.1 SLAs in Multi-layered Service Delivery Models; 2.2 Defining a KPI Framework for Business and IT
2.2.1 Business Versus Technical KPIs2.2.2 Types of KPI Measurements; 2.3 Distributed Service Level Management; 2.4 Complexity of SLA Translations and Mappings; 2.5 The Challenge for Efficient Service Level Objectives; 2.6 KPI Dependencies and Associations; 2.7 A Property Graph Model for KPI Relationships; 2.8 Example: SLA Translations Within a 4 Tier Web App; References; Chapter 3: Couplings: A Bi-polar Concept; 3.1 Dependence Coupling as Measurement; 3.2 Inductive Dependency Measurement: A Field Experiment; 3.2.1 Inductive Versus Deductive Measurement of Dependencies
3.2.2 Pilot Within a Flexible Hosting Data-Centre3.2.3 Assessment of Empirical Data Analysis; 3.2.4 Creating Dependency Rules out of Historical Data-Series; 3.3 Deductive Dependency Determination; 3.3.1 Selection of Measurement; 3.3.2 Traditional Static Software Coupling Calculations; 3.3.3 Advanced Dynamic Coupling Calculations; 3.4 Bi-polar Impact Aspects; 3.5 Measurements for Loose Coupling; 3.5.1 Overview; 3.5.2 Setting of Business Objectives; 3.5.3 Defining the Degree of Loose Coupling; References; Chapter 4: Classifying the Level of Coupling by Intuitionistic Fuzzy Sets
4.1 Describing KPI Qualities and Relationships by Fuzzy Methods4.1.1 Modelling of KPI Qualities Using Fuzzy Sets; 4.1.2 Model of KPI Relationships by Existing Fuzzy Methods; 4.1.2.1 Fuzzy Performance Relation Rules; 4.1.2.2 Fuzzy Cognitive Maps to Model KPI Dependencies; 4.2 Motivation on Intuitionistic Fuzzy Sets; 4.3 IFS Definition and Basic Operations; 4.4 Applying IFS to Service Dependencies and Impacts; 4.4.1 Mapping the Level of Coupling into IFS; 4.4.2 The Importance of the Unknown in the Middle; 4.4.3 Intuitionistic Fuzzy Direct Coupling Index (IFDCI)
4.4.3.1 Normalized Weights of Tight and Loose Coupling4.4.3.2 Pulling Tight and Loose Coupling into One IFS Called IFDCI; 4.5 Defining the Uncertainty; 4.6 Example for IFDCI Calculation Using Fuzzy Complements; 4.7 Intuitionistic Fuzzy Indirect Coupling Index (IFICI); 4.7.1 Calculating Indirect Couplings; 4.7.2 Types of Indirect Impact Operations; 4.7.3 Example of Indirect Coupling Calculations; 4.7.4 IFSFIA Formal Definition; 4.8 Semantics of Intuitionistic Fuzzy Dependencies; 4.9 Advantages of Atanassovs ́IFS; References
2.2.1 Business Versus Technical KPIs2.2.2 Types of KPI Measurements; 2.3 Distributed Service Level Management; 2.4 Complexity of SLA Translations and Mappings; 2.5 The Challenge for Efficient Service Level Objectives; 2.6 KPI Dependencies and Associations; 2.7 A Property Graph Model for KPI Relationships; 2.8 Example: SLA Translations Within a 4 Tier Web App; References; Chapter 3: Couplings: A Bi-polar Concept; 3.1 Dependence Coupling as Measurement; 3.2 Inductive Dependency Measurement: A Field Experiment; 3.2.1 Inductive Versus Deductive Measurement of Dependencies
3.2.2 Pilot Within a Flexible Hosting Data-Centre3.2.3 Assessment of Empirical Data Analysis; 3.2.4 Creating Dependency Rules out of Historical Data-Series; 3.3 Deductive Dependency Determination; 3.3.1 Selection of Measurement; 3.3.2 Traditional Static Software Coupling Calculations; 3.3.3 Advanced Dynamic Coupling Calculations; 3.4 Bi-polar Impact Aspects; 3.5 Measurements for Loose Coupling; 3.5.1 Overview; 3.5.2 Setting of Business Objectives; 3.5.3 Defining the Degree of Loose Coupling; References; Chapter 4: Classifying the Level of Coupling by Intuitionistic Fuzzy Sets
4.1 Describing KPI Qualities and Relationships by Fuzzy Methods4.1.1 Modelling of KPI Qualities Using Fuzzy Sets; 4.1.2 Model of KPI Relationships by Existing Fuzzy Methods; 4.1.2.1 Fuzzy Performance Relation Rules; 4.1.2.2 Fuzzy Cognitive Maps to Model KPI Dependencies; 4.2 Motivation on Intuitionistic Fuzzy Sets; 4.3 IFS Definition and Basic Operations; 4.4 Applying IFS to Service Dependencies and Impacts; 4.4.1 Mapping the Level of Coupling into IFS; 4.4.2 The Importance of the Unknown in the Middle; 4.4.3 Intuitionistic Fuzzy Direct Coupling Index (IFDCI)
4.4.3.1 Normalized Weights of Tight and Loose Coupling4.4.3.2 Pulling Tight and Loose Coupling into One IFS Called IFDCI; 4.5 Defining the Uncertainty; 4.6 Example for IFDCI Calculation Using Fuzzy Complements; 4.7 Intuitionistic Fuzzy Indirect Coupling Index (IFICI); 4.7.1 Calculating Indirect Couplings; 4.7.2 Types of Indirect Impact Operations; 4.7.3 Example of Indirect Coupling Calculations; 4.7.4 IFSFIA Formal Definition; 4.8 Semantics of Intuitionistic Fuzzy Dependencies; 4.9 Advantages of Atanassovs ́IFS; References