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Network and service coordination: conventional and machine learning approaches / Stefan Schneider ; Supervisor: Prof. Dr. Holger Karl. Paderborn, 2022
Content
Abstract
Introduction
Contributions
Publications
Projects
Thesis Structure and Overview
Background
Network Softwarization
Cloud and Edge Computing
Mobile Networks
Problem Statement
Problem Parameters
Network
Services
Traffic
Decision Variables
Service Scaling and Placement
Flow Scheduling
Routing
Optimization Objectives
Conventional Coordination Approaches
Centralized Coordination
Introduction
BSP Heuristic Approach
Initialization
Embedding Procedure
Iterative Improvement
Evaluation
Evaluation Setup
Comparison Against the MILP Approach
Scalability and Online Coordination
Conclusion
Hierarchical Coordination
Introduction
Related Work
Problem Statement
Hierarchical Coordination Approach
Domains and Hierarchies
Bottom-Up Information Advertisement (Phase 1)
Top-Down Coordination Decisions (Phase 2)
Mixed-Integer Linear Program (MILP)
Objective
Constraints
Evaluation
Evaluation Setup
Solution Quality
Runtime Analysis
Impact of Advertised Information
Conclusion
Fully Distributed Coordination
Introduction
Related Work
Problem Statement
Fully Distributed Coordination Approaches
Greedy Coordination with Adaptive Shortest Paths
Score-Based Coordination with Adaptive Shortest Paths
Evaluation
Evaluation Setup
Solution Quality
Coordination Stability
Scalability
Conclusion
Machine Learning Coordination Approaches
Machine Learning for Dynamic Resource Allocation
Introduction
Related Work
Approaches Without Machine Learning
Machine Learning Approaches
Problem Statement
Machine Learning Approach
Overview
Performance Profiling
Learning Resource Requirements
Integration with Coordination Approaches
Evaluation
Approximation Accuracy
Impact on Network and Service Coordination
Runtime Analysis
Conclusion
Self-Learning Coordination
Introduction
Related Work
Conventional Approaches Without DRL
Self-Learning DRL Approaches
Problem Statement
DeepCoord DRL Approach
Joint Scheduling, Scaling, and Placement
Partially Observable Markov Decision Process (POMDP)
DRL Coordination Framework
Implementation and Deployment
Evaluation
Evaluation Setup
Self-Adaptation to Varying Scenarios
Generalization to Unseen Scenarios
Optimizing Multiple Objectives
Scalability
Conclusion
Self-Learning Distributed Coordination
Introduction
Related Work
Problem Statement
Distributed DRL Approach
Joint Scaling, Placement, Scheduling, and Routing
Partially Observable Markov Decision Process (POMDP)
DRL Coordination Framework
Evaluation
Evaluation Setup
Varying Traffic Patterns
Varying Deadlines
Generalization to Unseen Scenarios
Scalability
Conclusion
Self-Learning Coordination for Mobile Networks
Introduction
Related Work
Conventional Approaches Without DRL
Self-Learning DRL Approaches
Problem Statement
Parameters
Decision Variables
Objective: Optimize Quality of Experience (QoE)
DeepCoMP: Centralized DRL
Partially Observable Markov Decision Process (POMDP)
DeepCoMP Algorithm
DD-CoMP & D3-CoMP: Distributed DRL
Trade-Offs and Design Choices
Partially Observable Markov Decision Process (POMDP)
DD-CoMP Algorithm
D3-CoMP Algorithm
Architecture and Deployment Options
Network-Based Deployment
UE-Based Deployment
Training and Inference in Practice
Evaluation
Evaluation Setup
Self-Adaption to Varying Scenarios
Generalization and Transfer Learning
Scalability
Conclusion
Future Research and Conclusion
Future Research
Conclusion
Acknowledgments
Acronyms
List of Algorithms
List of Figures
List of Tables
Bibliography: Own Work
Bibliography: Others' Work
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