Improving service level engineering : an intuitionistic fuzzy approach / Roland Schütze.
This book examines how fuzzy methods can be employed to manage service levels in business and IT alignment. It starts by mapping the dependencies of service level agreements, coming up with gradual and bi-polar concepts to eventually classify the level of coupling by intuitionistic fuzzy sets. The s...
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Full Text (via Springer) |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
[2018]
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| Series: | Fuzzy management methods.
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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.