Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ
John Wiley & Sons, Incorporated
2021
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| Series: | IEEE Press Series on Networks and Service Management Ser
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| Online Access: | FHI01 FHR01 |
| Item Description: | Description based on publisher supplied metadata and other sources |
| Physical Description: | 1 Online-Ressource (xxxi, 361 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781119675440 |
Staff View
MARC
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| 245 | 1 | 0 | |a Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- List of Contributors -- Preface -- Acknowledgments -- Acronyms -- Part I Introduction -- Chapter 1 Overview of Network and Service Management -- 1.1 Network and Service Management at Large -- 1.2 Data Collection and Monitoring Protocols -- 1.2.1 SNMP Protocol Family -- 1.2.2 Syslog Protocol -- 1.2.3 IP Flow Information eXport (IPFIX) -- 1.2.4 IP Performance Metrics (IPPM) -- 1.2.5 Routing Protocols and Monitoring Platforms -- 1.3 Network Configuration Protocol -- 1.3.1 Standard Configuration Protocols and Approaches -- 1.3.2 Proprietary Configuration Protocols -- 1.3.3 Integrated Platforms for Network Monitoring -- 1.4 Novel Solutions and Scenarios -- 1.4.1 Software‐Defined Networking - SDN -- 1.4.2 Network Functions Virtualization - NFV -- Bibliography -- Chapter 2 Overview of Artificial Intelligence and Machine Learning -- 2.1 Overview -- 2.2 Learning Algorithms -- 2.2.1 Supervised Learning -- 2.2.2 Unsupervised Learning -- 2.2.3 Reinforcement Learning -- 2.3 Learning for Network and Service Management -- Bibliography -- Part II Management Models and Frameworks -- Chapter 3 Managing Virtualized Networks and Services with Machine Learning -- 3.1 Introduction -- 3.2 Technology Overview -- 3.2.1 Virtualization of Network Functions -- 3.2.1.1 Resource Partitioning -- 3.2.1.2 Virtualized Network Functions -- 3.2.2 Link Virtualization -- 3.2.2.1 Physical Layer Partitioning -- 3.2.2.2 Virtualization at Higher Layers -- 3.2.3 Network Virtualization -- 3.2.4 Network Slicing -- 3.2.5 Management and Orchestration -- 3.3 State‐of‐the‐Art -- 3.3.1 Network Virtualization -- 3.3.2 Network Functions Virtualization -- 3.3.2.1 Placement -- 3.3.2.2 Scaling -- 3.3.3 Network Slicing -- 3.3.3.1 Admission Control -- 3.3.3.2 Resource Allocation -- 3.4 Conclusion and Future Direction | |
| 505 | 8 | |a 3.4.1 Intelligent Monitoring -- 3.4.2 Seamless Operation and Maintenance -- 3.4.3 Dynamic Slice Orchestration -- 3.4.4 Automated Failure Management -- 3.4.5 Adaptation and Consolidation of Resources -- 3.4.6 Sensitivity to Heterogeneous Hardware -- 3.4.7 Securing Machine Learning -- Bibliography -- Chapter 4 Self‐Managed 5G Networks1 -- 4.1 Introduction -- 4.2 Technology Overview -- 4.2.1 RAN Virtualization and Management -- 4.2.2 Network Function Virtualization -- 4.2.3 Data Plane Programmability -- 4.2.4 Programmable Optical Switches -- 4.2.5 Network Data Management -- 4.3 5G Management State‐of‐the‐Art -- 4.3.1 RAN resource management -- 4.3.1.1 Context‐Based Clustering and Profiling for User and Network Devices -- 4.3.1.2 Q‐Learning Based RAN Resource Allocation -- 4.3.1.3 vrAIn: AI‐Assisted Resource Orchestration for Virtualized Radio Access Networks -- 4.3.2 Service Orchestration -- 4.3.3 Data Plane Slicing and Programmable Traffic Management -- 4.3.4 Wavelength Allocation -- 4.3.5 Federation -- 4.4 Conclusions and Future Directions -- Bibliography -- Chapter 5 AI in 5G Networks: Challenges and Use Cases -- 5.1 Introduction -- 5.2 Background -- 5.2.1 ML in the Networking Context -- 5.2.2 ML in Virtualized Networks -- 5.2.3 ML for QoE Assessment and Management -- 5.3 Case Studies -- 5.3.1 QoE Estimation and Management -- 5.3.1.1 Main Challenges -- 5.3.1.2 Methodology -- 5.3.1.3 Results and Guidelines -- 5.3.2 Proactive VNF Deployment -- 5.3.2.1 Problem Statement and Main Challenges -- 5.3.2.2 Methodology -- 5.3.2.3 Evaluation Results and Guidelines -- 5.3.3 Multi‐service, Multi‐domain Interconnect -- 5.4 Conclusions and Future Directions -- Bibliography -- Chapter 6 Machine Learning for Resource Allocation in Mobile Broadband Networks -- 6.1 Introduction -- 6.2 ML in Wireless Networks -- 6.2.1 Supervised ML -- 6.2.1.1 Classification Techniques | |
| 505 | 8 | |a 6.2.1.2 Regression Techniques -- 6.2.2 Unsupervised ML -- 6.2.2.1 Clustering Techniques -- 6.2.2.2 Soft Clustering Techniques -- 6.2.3 Reinforcement Learning -- 6.2.4 Deep Learning -- 6.2.5 Summary -- 6.3 ML‐Enabled Resource Allocation -- 6.3.1 Power Control -- 6.3.1.1 Overview -- 6.3.1.2 State‐of‐the‐Art -- 6.3.1.3 Lessons Learnt -- 6.3.2 Scheduling -- 6.3.2.1 Overview -- 6.3.2.2 State‐of‐the‐Art -- 6.3.2.3 Lessons Learnt -- 6.3.3 User Association -- 6.3.3.1 Overview -- 6.3.3.2 State‐of‐the‐Art -- 6.3.3.3 Lessons Learnt -- 6.3.4 Spectrum Allocation -- 6.3.4.1 Overview -- 6.3.4.2 State‐of‐the‐Art -- 6.3.4.3 Lessons Learnt -- 6.4 Conclusion and Future Directions -- 6.4.1 Transfer Learning -- 6.4.2 Imitation Learning -- 6.4.3 Federated‐Edge Learning -- 6.4.4 Quantum Machine Learning -- Bibliography -- Chapter 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing -- 7.1 Introduction -- 7.2 Technology Overview -- 7.2.1 Fog Computing (FC) -- 7.2.2 Resource Provisioning -- 7.2.3 Service Function Chaining (SFC) -- 7.2.4 Micro‐service Architecture -- 7.2.5 Reinforcement Learning (RL) -- 7.3 State‐of‐the‐Art -- 7.3.1 Resource Allocation for Fog Computing -- 7.3.2 ML Techniques for Resource Allocation -- 7.3.3 RL Methods for Resource Allocation -- 7.4 A RL Approach for SFC Allocation in Fog Computing -- 7.4.1 Problem Formulation -- 7.4.2 Observation Space -- 7.4.3 Action Space -- 7.4.4 Reward Function -- 7.4.5 Agent -- 7.5 Evaluation Setup -- 7.5.1 Fog-Cloud Infrastructure -- 7.5.2 Environment Implementation -- 7.5.3 Environment Configuration -- 7.6 Results -- 7.6.1 Static Scenario -- 7.6.2 Dynamic Scenario -- 7.7 Conclusion and Future Direction -- Bibliography -- Part III Management Functions and Applications -- Chapter 8 Designing Algorithms for Data‐Driven Network Management and Control: State‐of‐the‐Art and Challenges1 | |
| 505 | 8 | |a 8.1 Introduction -- 8.1.1 Contributions -- 8.1.2 Exemplary Network Use Case Study -- 8.2 Technology Overview -- 8.2.1 Data‐Driven Network Optimization -- 8.2.2 Optimization Problems over Graphs -- 8.2.3 From Graphs to ML/AI Input -- 8.2.4 End‐to‐End Learning -- 8.3 Data‐Driven Algorithm Design: State‐of‐the Art -- 8.3.1 Data‐Driven Optimization in General -- 8.3.2 Data‐Driven Network Optimization -- 8.3.3 Non‐graph Related Problems -- 8.4 Future Direction -- 8.4.1 Data Production and Collection -- 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees -- 8.5 Summary -- Acknowledgments -- Bibliography -- Chapter 9 AI‐Driven Performance Management in Data‐Intensive Applications -- 9.1 Introduction -- 9.2 Data‐Processing Frameworks -- 9.2.1 Apache Storm -- 9.2.2 Hadoop MapReduce -- 9.2.3 Apache Spark -- 9.2.4 Apache Flink -- 9.3 State‐of‐the‐Art -- 9.3.1 Optimal Configuration -- 9.3.1.1 Traditional Approaches -- 9.3.1.2 AI Approaches -- 9.3.1.3 Example: AI‐Based Optimal Configuration -- 9.3.2 Performance Anomaly Detection -- 9.3.2.1 Traditional Approaches -- 9.3.2.2 AI Approaches -- 9.3.2.3 Example: ANNs‐Based Anomaly Detection -- 9.3.3 Load Prediction -- 9.3.3.1 Traditional Approaches -- 9.3.3.2 AI Approaches -- 9.3.4 Scaling Techniques -- 9.3.4.1 Traditional Approaches -- 9.3.4.2 AI Approaches -- 9.3.5 Example: RL‐Based Auto‐scaling Policies -- 9.4 Conclusion and Future Direction -- Bibliography -- Chapter 10 Datacenter Traffic Optimization with Deep Reinforcement Learning -- 10.1 Introduction -- 10.2 Technology Overview -- 10.2.1 Deep Reinforcement Learning (DRL) -- 10.2.2 Applying ML to Networks -- 10.2.3 Traffic Optimization Approaches in Datacenter -- 10.2.4 Example: DRL for Flow Scheduling -- 10.2.4.1 Flow Scheduling Problem -- 10.2.4.2 DRL Formulation -- 10.2.4.3 DRL Algorithm -- 10.3 State‐of‐the‐Art: AuTO Design | |
| 505 | 8 | |a 10.3.1 Problem Identified -- 10.3.2 Overview -- 10.3.3 Peripheral System -- 10.3.3.1 Enforcement Module -- 10.3.3.2 Monitoring Module -- 10.3.4 Central System -- 10.3.5 DRL Formulations and Solutions -- 10.3.5.1 Optimizing MLFQ Thresholds -- 10.3.5.2 Optimizing Long Flows -- 10.4 Implementation -- 10.4.1 Peripheral System -- 10.4.1.1 Monitoring Module (MM): -- 10.4.1.2 Enforcement Module (EM): -- 10.4.2 Central System -- 10.4.2.1 sRLA -- 10.4.2.2 lRLA -- 10.5 Experimental Results -- 10.5.1 Setting -- 10.5.2 Comparison Targets -- 10.5.3 Experiments -- 10.5.3.1 Homogeneous Traffic -- 10.5.3.2 Spatially Heterogeneous Traffic -- 10.5.3.3 Temporally and Spatially Heterogeneous Traffic -- 10.5.4 Deep Dive -- 10.5.4.1 Optimizing MLFQ Thresholds using DRL -- 10.5.4.2 Optimizing Long Flows using DRL -- 10.5.4.3 System Overhead -- 10.6 Conclusion and Future Directions -- Bibliography -- Chapter 11 The New Abnormal: Network Anomalies in the AI Era -- 11.1 Introduction -- 11.2 Definitions and Classic Approaches -- 11.2.1 Definitions -- 11.2.2 Anomaly Detection: A Taxonomy -- 11.2.3 Problem Characteristics -- 11.2.4 Classic Approaches -- 11.3 AI and Anomaly Detection -- 11.3.1 Methodology -- 11.3.2 Deep Neural Networks -- 11.3.3 Representation Learning -- 11.3.4 Autoencoders -- 11.3.5 Generative Adversarial Networks -- 11.3.6 Reinforcement Learning -- 11.3.7 Summary and Takeaways -- 11.4 Technology Overview -- 11.4.1 Production‐Ready Tools -- 11.4.2 Research Alternatives -- 11.4.3 Summary and Takeaways -- 11.5 Conclusions and Future Directions -- Bibliography -- Chapter 12 Automated Orchestration of Security Chains Driven by Process Learning* -- 12.1 Introduction -- 12.2 Related Work -- 12.2.1 Chains of Security Functions -- 12.2.2 Formal Verification of Networking Policies -- 12.3 Background -- 12.3.1 Flow‐Based Detection of Attacks | |
| 505 | 8 | |a 12.3.2 Programming SDN Controllers | |
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| contents | Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- List of Contributors -- Preface -- Acknowledgments -- Acronyms -- Part I Introduction -- Chapter 1 Overview of Network and Service Management -- 1.1 Network and Service Management at Large -- 1.2 Data Collection and Monitoring Protocols -- 1.2.1 SNMP Protocol Family -- 1.2.2 Syslog Protocol -- 1.2.3 IP Flow Information eXport (IPFIX) -- 1.2.4 IP Performance Metrics (IPPM) -- 1.2.5 Routing Protocols and Monitoring Platforms -- 1.3 Network Configuration Protocol -- 1.3.1 Standard Configuration Protocols and Approaches -- 1.3.2 Proprietary Configuration Protocols -- 1.3.3 Integrated Platforms for Network Monitoring -- 1.4 Novel Solutions and Scenarios -- 1.4.1 Software‐Defined Networking - SDN -- 1.4.2 Network Functions Virtualization - NFV -- Bibliography -- Chapter 2 Overview of Artificial Intelligence and Machine Learning -- 2.1 Overview -- 2.2 Learning Algorithms -- 2.2.1 Supervised Learning -- 2.2.2 Unsupervised Learning -- 2.2.3 Reinforcement Learning -- 2.3 Learning for Network and Service Management -- Bibliography -- Part II Management Models and Frameworks -- Chapter 3 Managing Virtualized Networks and Services with Machine Learning -- 3.1 Introduction -- 3.2 Technology Overview -- 3.2.1 Virtualization of Network Functions -- 3.2.1.1 Resource Partitioning -- 3.2.1.2 Virtualized Network Functions -- 3.2.2 Link Virtualization -- 3.2.2.1 Physical Layer Partitioning -- 3.2.2.2 Virtualization at Higher Layers -- 3.2.3 Network Virtualization -- 3.2.4 Network Slicing -- 3.2.5 Management and Orchestration -- 3.3 State‐of‐the‐Art -- 3.3.1 Network Virtualization -- 3.3.2 Network Functions Virtualization -- 3.3.2.1 Placement -- 3.3.2.2 Scaling -- 3.3.3 Network Slicing -- 3.3.3.1 Admission Control -- 3.3.3.2 Resource Allocation -- 3.4 Conclusion and Future Direction 3.4.1 Intelligent Monitoring -- 3.4.2 Seamless Operation and Maintenance -- 3.4.3 Dynamic Slice Orchestration -- 3.4.4 Automated Failure Management -- 3.4.5 Adaptation and Consolidation of Resources -- 3.4.6 Sensitivity to Heterogeneous Hardware -- 3.4.7 Securing Machine Learning -- Bibliography -- Chapter 4 Self‐Managed 5G Networks1 -- 4.1 Introduction -- 4.2 Technology Overview -- 4.2.1 RAN Virtualization and Management -- 4.2.2 Network Function Virtualization -- 4.2.3 Data Plane Programmability -- 4.2.4 Programmable Optical Switches -- 4.2.5 Network Data Management -- 4.3 5G Management State‐of‐the‐Art -- 4.3.1 RAN resource management -- 4.3.1.1 Context‐Based Clustering and Profiling for User and Network Devices -- 4.3.1.2 Q‐Learning Based RAN Resource Allocation -- 4.3.1.3 vrAIn: AI‐Assisted Resource Orchestration for Virtualized Radio Access Networks -- 4.3.2 Service Orchestration -- 4.3.3 Data Plane Slicing and Programmable Traffic Management -- 4.3.4 Wavelength Allocation -- 4.3.5 Federation -- 4.4 Conclusions and Future Directions -- Bibliography -- Chapter 5 AI in 5G Networks: Challenges and Use Cases -- 5.1 Introduction -- 5.2 Background -- 5.2.1 ML in the Networking Context -- 5.2.2 ML in Virtualized Networks -- 5.2.3 ML for QoE Assessment and Management -- 5.3 Case Studies -- 5.3.1 QoE Estimation and Management -- 5.3.1.1 Main Challenges -- 5.3.1.2 Methodology -- 5.3.1.3 Results and Guidelines -- 5.3.2 Proactive VNF Deployment -- 5.3.2.1 Problem Statement and Main Challenges -- 5.3.2.2 Methodology -- 5.3.2.3 Evaluation Results and Guidelines -- 5.3.3 Multi‐service, Multi‐domain Interconnect -- 5.4 Conclusions and Future Directions -- Bibliography -- Chapter 6 Machine Learning for Resource Allocation in Mobile Broadband Networks -- 6.1 Introduction -- 6.2 ML in Wireless Networks -- 6.2.1 Supervised ML -- 6.2.1.1 Classification Techniques 6.2.1.2 Regression Techniques -- 6.2.2 Unsupervised ML -- 6.2.2.1 Clustering Techniques -- 6.2.2.2 Soft Clustering Techniques -- 6.2.3 Reinforcement Learning -- 6.2.4 Deep Learning -- 6.2.5 Summary -- 6.3 ML‐Enabled Resource Allocation -- 6.3.1 Power Control -- 6.3.1.1 Overview -- 6.3.1.2 State‐of‐the‐Art -- 6.3.1.3 Lessons Learnt -- 6.3.2 Scheduling -- 6.3.2.1 Overview -- 6.3.2.2 State‐of‐the‐Art -- 6.3.2.3 Lessons Learnt -- 6.3.3 User Association -- 6.3.3.1 Overview -- 6.3.3.2 State‐of‐the‐Art -- 6.3.3.3 Lessons Learnt -- 6.3.4 Spectrum Allocation -- 6.3.4.1 Overview -- 6.3.4.2 State‐of‐the‐Art -- 6.3.4.3 Lessons Learnt -- 6.4 Conclusion and Future Directions -- 6.4.1 Transfer Learning -- 6.4.2 Imitation Learning -- 6.4.3 Federated‐Edge Learning -- 6.4.4 Quantum Machine Learning -- Bibliography -- Chapter 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing -- 7.1 Introduction -- 7.2 Technology Overview -- 7.2.1 Fog Computing (FC) -- 7.2.2 Resource Provisioning -- 7.2.3 Service Function Chaining (SFC) -- 7.2.4 Micro‐service Architecture -- 7.2.5 Reinforcement Learning (RL) -- 7.3 State‐of‐the‐Art -- 7.3.1 Resource Allocation for Fog Computing -- 7.3.2 ML Techniques for Resource Allocation -- 7.3.3 RL Methods for Resource Allocation -- 7.4 A RL Approach for SFC Allocation in Fog Computing -- 7.4.1 Problem Formulation -- 7.4.2 Observation Space -- 7.4.3 Action Space -- 7.4.4 Reward Function -- 7.4.5 Agent -- 7.5 Evaluation Setup -- 7.5.1 Fog-Cloud Infrastructure -- 7.5.2 Environment Implementation -- 7.5.3 Environment Configuration -- 7.6 Results -- 7.6.1 Static Scenario -- 7.6.2 Dynamic Scenario -- 7.7 Conclusion and Future Direction -- Bibliography -- Part III Management Functions and Applications -- Chapter 8 Designing Algorithms for Data‐Driven Network Management and Control: State‐of‐the‐Art and Challenges1 8.1 Introduction -- 8.1.1 Contributions -- 8.1.2 Exemplary Network Use Case Study -- 8.2 Technology Overview -- 8.2.1 Data‐Driven Network Optimization -- 8.2.2 Optimization Problems over Graphs -- 8.2.3 From Graphs to ML/AI Input -- 8.2.4 End‐to‐End Learning -- 8.3 Data‐Driven Algorithm Design: State‐of‐the Art -- 8.3.1 Data‐Driven Optimization in General -- 8.3.2 Data‐Driven Network Optimization -- 8.3.3 Non‐graph Related Problems -- 8.4 Future Direction -- 8.4.1 Data Production and Collection -- 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees -- 8.5 Summary -- Acknowledgments -- Bibliography -- Chapter 9 AI‐Driven Performance Management in Data‐Intensive Applications -- 9.1 Introduction -- 9.2 Data‐Processing Frameworks -- 9.2.1 Apache Storm -- 9.2.2 Hadoop MapReduce -- 9.2.3 Apache Spark -- 9.2.4 Apache Flink -- 9.3 State‐of‐the‐Art -- 9.3.1 Optimal Configuration -- 9.3.1.1 Traditional Approaches -- 9.3.1.2 AI Approaches -- 9.3.1.3 Example: AI‐Based Optimal Configuration -- 9.3.2 Performance Anomaly Detection -- 9.3.2.1 Traditional Approaches -- 9.3.2.2 AI Approaches -- 9.3.2.3 Example: ANNs‐Based Anomaly Detection -- 9.3.3 Load Prediction -- 9.3.3.1 Traditional Approaches -- 9.3.3.2 AI Approaches -- 9.3.4 Scaling Techniques -- 9.3.4.1 Traditional Approaches -- 9.3.4.2 AI Approaches -- 9.3.5 Example: RL‐Based Auto‐scaling Policies -- 9.4 Conclusion and Future Direction -- Bibliography -- Chapter 10 Datacenter Traffic Optimization with Deep Reinforcement Learning -- 10.1 Introduction -- 10.2 Technology Overview -- 10.2.1 Deep Reinforcement Learning (DRL) -- 10.2.2 Applying ML to Networks -- 10.2.3 Traffic Optimization Approaches in Datacenter -- 10.2.4 Example: DRL for Flow Scheduling -- 10.2.4.1 Flow Scheduling Problem -- 10.2.4.2 DRL Formulation -- 10.2.4.3 DRL Algorithm -- 10.3 State‐of‐the‐Art: AuTO Design 10.3.1 Problem Identified -- 10.3.2 Overview -- 10.3.3 Peripheral System -- 10.3.3.1 Enforcement Module -- 10.3.3.2 Monitoring Module -- 10.3.4 Central System -- 10.3.5 DRL Formulations and Solutions -- 10.3.5.1 Optimizing MLFQ Thresholds -- 10.3.5.2 Optimizing Long Flows -- 10.4 Implementation -- 10.4.1 Peripheral System -- 10.4.1.1 Monitoring Module (MM): -- 10.4.1.2 Enforcement Module (EM): -- 10.4.2 Central System -- 10.4.2.1 sRLA -- 10.4.2.2 lRLA -- 10.5 Experimental Results -- 10.5.1 Setting -- 10.5.2 Comparison Targets -- 10.5.3 Experiments -- 10.5.3.1 Homogeneous Traffic -- 10.5.3.2 Spatially Heterogeneous Traffic -- 10.5.3.3 Temporally and Spatially Heterogeneous Traffic -- 10.5.4 Deep Dive -- 10.5.4.1 Optimizing MLFQ Thresholds using DRL -- 10.5.4.2 Optimizing Long Flows using DRL -- 10.5.4.3 System Overhead -- 10.6 Conclusion and Future Directions -- Bibliography -- Chapter 11 The New Abnormal: Network Anomalies in the AI Era -- 11.1 Introduction -- 11.2 Definitions and Classic Approaches -- 11.2.1 Definitions -- 11.2.2 Anomaly Detection: A Taxonomy -- 11.2.3 Problem Characteristics -- 11.2.4 Classic Approaches -- 11.3 AI and Anomaly Detection -- 11.3.1 Methodology -- 11.3.2 Deep Neural Networks -- 11.3.3 Representation Learning -- 11.3.4 Autoencoders -- 11.3.5 Generative Adversarial Networks -- 11.3.6 Reinforcement Learning -- 11.3.7 Summary and Takeaways -- 11.4 Technology Overview -- 11.4.1 Production‐Ready Tools -- 11.4.2 Research Alternatives -- 11.4.3 Summary and Takeaways -- 11.5 Conclusions and Future Directions -- Bibliography -- Chapter 12 Automated Orchestration of Security Chains Driven by Process Learning* -- 12.1 Introduction -- 12.2 Related Work -- 12.2.1 Chains of Security Functions -- 12.2.2 Formal Verification of Networking Policies -- 12.3 Background -- 12.3.1 Flow‐Based Detection of Attacks 12.3.2 Programming SDN Controllers |
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| dewey-full | 004.6 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004.6 |
| dewey-search | 004.6 |
| dewey-sort | 14.6 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Electronic eBook |
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ind1="8" ind2=" "><subfield code="a">Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- List of Contributors -- Preface -- Acknowledgments -- Acronyms -- Part I Introduction -- Chapter 1 Overview of Network and Service Management -- 1.1 Network and Service Management at Large -- 1.2 Data Collection and Monitoring Protocols -- 1.2.1 SNMP Protocol Family -- 1.2.2 Syslog Protocol -- 1.2.3 IP Flow Information eXport (IPFIX) -- 1.2.4 IP Performance Metrics (IPPM) -- 1.2.5 Routing Protocols and Monitoring Platforms -- 1.3 Network Configuration Protocol -- 1.3.1 Standard Configuration Protocols and Approaches -- 1.3.2 Proprietary Configuration Protocols -- 1.3.3 Integrated Platforms for Network Monitoring -- 1.4 Novel Solutions and Scenarios -- 1.4.1 Software‐Defined Networking - SDN -- 1.4.2 Network Functions Virtualization - NFV -- Bibliography -- Chapter 2 Overview of Artificial Intelligence and Machine Learning -- 2.1 Overview -- 2.2 Learning Algorithms -- 2.2.1 Supervised Learning -- 2.2.2 Unsupervised Learning -- 2.2.3 Reinforcement Learning -- 2.3 Learning for Network and Service Management -- Bibliography -- Part II Management Models and Frameworks -- Chapter 3 Managing Virtualized Networks and Services with Machine Learning -- 3.1 Introduction -- 3.2 Technology Overview -- 3.2.1 Virtualization of Network Functions -- 3.2.1.1 Resource Partitioning -- 3.2.1.2 Virtualized Network Functions -- 3.2.2 Link Virtualization -- 3.2.2.1 Physical Layer Partitioning -- 3.2.2.2 Virtualization at Higher Layers -- 3.2.3 Network Virtualization -- 3.2.4 Network Slicing -- 3.2.5 Management and Orchestration -- 3.3 State‐of‐the‐Art -- 3.3.1 Network Virtualization -- 3.3.2 Network Functions Virtualization -- 3.3.2.1 Placement -- 3.3.2.2 Scaling -- 3.3.3 Network Slicing -- 3.3.3.1 Admission Control -- 3.3.3.2 Resource Allocation -- 3.4 Conclusion and Future Direction</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.4.1 Intelligent Monitoring -- 3.4.2 Seamless Operation and Maintenance -- 3.4.3 Dynamic Slice Orchestration -- 3.4.4 Automated Failure Management -- 3.4.5 Adaptation and Consolidation of Resources -- 3.4.6 Sensitivity to Heterogeneous Hardware -- 3.4.7 Securing Machine Learning -- Bibliography -- Chapter 4 Self‐Managed 5G Networks1 -- 4.1 Introduction -- 4.2 Technology Overview -- 4.2.1 RAN Virtualization and Management -- 4.2.2 Network Function Virtualization -- 4.2.3 Data Plane Programmability -- 4.2.4 Programmable Optical Switches -- 4.2.5 Network Data Management -- 4.3 5G Management State‐of‐the‐Art -- 4.3.1 RAN resource management -- 4.3.1.1 Context‐Based Clustering and Profiling for User and Network Devices -- 4.3.1.2 Q‐Learning Based RAN Resource Allocation -- 4.3.1.3 vrAIn: AI‐Assisted Resource Orchestration for Virtualized Radio Access Networks -- 4.3.2 Service Orchestration -- 4.3.3 Data Plane Slicing and Programmable Traffic Management -- 4.3.4 Wavelength Allocation -- 4.3.5 Federation -- 4.4 Conclusions and Future Directions -- Bibliography -- Chapter 5 AI in 5G Networks: Challenges and Use Cases -- 5.1 Introduction -- 5.2 Background -- 5.2.1 ML in the Networking Context -- 5.2.2 ML in Virtualized Networks -- 5.2.3 ML for QoE Assessment and Management -- 5.3 Case Studies -- 5.3.1 QoE Estimation and Management -- 5.3.1.1 Main Challenges -- 5.3.1.2 Methodology -- 5.3.1.3 Results and Guidelines -- 5.3.2 Proactive VNF Deployment -- 5.3.2.1 Problem Statement and Main Challenges -- 5.3.2.2 Methodology -- 5.3.2.3 Evaluation Results and Guidelines -- 5.3.3 Multi‐service, Multi‐domain Interconnect -- 5.4 Conclusions and Future Directions -- Bibliography -- Chapter 6 Machine Learning for Resource Allocation in Mobile Broadband Networks -- 6.1 Introduction -- 6.2 ML in Wireless Networks -- 6.2.1 Supervised ML -- 6.2.1.1 Classification Techniques</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">6.2.1.2 Regression Techniques -- 6.2.2 Unsupervised ML -- 6.2.2.1 Clustering Techniques -- 6.2.2.2 Soft Clustering Techniques -- 6.2.3 Reinforcement Learning -- 6.2.4 Deep Learning -- 6.2.5 Summary -- 6.3 ML‐Enabled Resource Allocation -- 6.3.1 Power Control -- 6.3.1.1 Overview -- 6.3.1.2 State‐of‐the‐Art -- 6.3.1.3 Lessons Learnt -- 6.3.2 Scheduling -- 6.3.2.1 Overview -- 6.3.2.2 State‐of‐the‐Art -- 6.3.2.3 Lessons Learnt -- 6.3.3 User Association -- 6.3.3.1 Overview -- 6.3.3.2 State‐of‐the‐Art -- 6.3.3.3 Lessons Learnt -- 6.3.4 Spectrum Allocation -- 6.3.4.1 Overview -- 6.3.4.2 State‐of‐the‐Art -- 6.3.4.3 Lessons Learnt -- 6.4 Conclusion and Future Directions -- 6.4.1 Transfer Learning -- 6.4.2 Imitation Learning -- 6.4.3 Federated‐Edge Learning -- 6.4.4 Quantum Machine Learning -- Bibliography -- Chapter 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing -- 7.1 Introduction -- 7.2 Technology Overview -- 7.2.1 Fog Computing (FC) -- 7.2.2 Resource Provisioning -- 7.2.3 Service Function Chaining (SFC) -- 7.2.4 Micro‐service Architecture -- 7.2.5 Reinforcement Learning (RL) -- 7.3 State‐of‐the‐Art -- 7.3.1 Resource Allocation for Fog Computing -- 7.3.2 ML Techniques for Resource Allocation -- 7.3.3 RL Methods for Resource Allocation -- 7.4 A RL Approach for SFC Allocation in Fog Computing -- 7.4.1 Problem Formulation -- 7.4.2 Observation Space -- 7.4.3 Action Space -- 7.4.4 Reward Function -- 7.4.5 Agent -- 7.5 Evaluation Setup -- 7.5.1 Fog-Cloud Infrastructure -- 7.5.2 Environment Implementation -- 7.5.3 Environment Configuration -- 7.6 Results -- 7.6.1 Static Scenario -- 7.6.2 Dynamic Scenario -- 7.7 Conclusion and Future Direction -- Bibliography -- Part III Management Functions and Applications -- Chapter 8 Designing Algorithms for Data‐Driven Network Management and Control: State‐of‐the‐Art and Challenges1</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.1 Introduction -- 8.1.1 Contributions -- 8.1.2 Exemplary Network Use Case Study -- 8.2 Technology Overview -- 8.2.1 Data‐Driven Network Optimization -- 8.2.2 Optimization Problems over Graphs -- 8.2.3 From Graphs to ML/AI Input -- 8.2.4 End‐to‐End Learning -- 8.3 Data‐Driven Algorithm Design: State‐of‐the Art -- 8.3.1 Data‐Driven Optimization in General -- 8.3.2 Data‐Driven Network Optimization -- 8.3.3 Non‐graph Related Problems -- 8.4 Future Direction -- 8.4.1 Data Production and Collection -- 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees -- 8.5 Summary -- Acknowledgments -- Bibliography -- Chapter 9 AI‐Driven Performance Management in Data‐Intensive Applications -- 9.1 Introduction -- 9.2 Data‐Processing Frameworks -- 9.2.1 Apache Storm -- 9.2.2 Hadoop MapReduce -- 9.2.3 Apache Spark -- 9.2.4 Apache Flink -- 9.3 State‐of‐the‐Art -- 9.3.1 Optimal Configuration -- 9.3.1.1 Traditional Approaches -- 9.3.1.2 AI Approaches -- 9.3.1.3 Example: AI‐Based Optimal Configuration -- 9.3.2 Performance Anomaly Detection -- 9.3.2.1 Traditional Approaches -- 9.3.2.2 AI Approaches -- 9.3.2.3 Example: ANNs‐Based Anomaly Detection -- 9.3.3 Load Prediction -- 9.3.3.1 Traditional Approaches -- 9.3.3.2 AI Approaches -- 9.3.4 Scaling Techniques -- 9.3.4.1 Traditional Approaches -- 9.3.4.2 AI Approaches -- 9.3.5 Example: RL‐Based Auto‐scaling Policies -- 9.4 Conclusion and Future Direction -- Bibliography -- Chapter 10 Datacenter Traffic Optimization with Deep Reinforcement Learning -- 10.1 Introduction -- 10.2 Technology Overview -- 10.2.1 Deep Reinforcement Learning (DRL) -- 10.2.2 Applying ML to Networks -- 10.2.3 Traffic Optimization Approaches in Datacenter -- 10.2.4 Example: DRL for Flow Scheduling -- 10.2.4.1 Flow Scheduling Problem -- 10.2.4.2 DRL Formulation -- 10.2.4.3 DRL Algorithm -- 10.3 State‐of‐the‐Art: AuTO Design</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">10.3.1 Problem Identified -- 10.3.2 Overview -- 10.3.3 Peripheral System -- 10.3.3.1 Enforcement Module -- 10.3.3.2 Monitoring Module -- 10.3.4 Central System -- 10.3.5 DRL Formulations and Solutions -- 10.3.5.1 Optimizing MLFQ Thresholds -- 10.3.5.2 Optimizing Long Flows -- 10.4 Implementation -- 10.4.1 Peripheral System -- 10.4.1.1 Monitoring Module (MM): -- 10.4.1.2 Enforcement Module (EM): -- 10.4.2 Central System -- 10.4.2.1 sRLA -- 10.4.2.2 lRLA -- 10.5 Experimental Results -- 10.5.1 Setting -- 10.5.2 Comparison Targets -- 10.5.3 Experiments -- 10.5.3.1 Homogeneous Traffic -- 10.5.3.2 Spatially Heterogeneous Traffic -- 10.5.3.3 Temporally and Spatially Heterogeneous Traffic -- 10.5.4 Deep Dive -- 10.5.4.1 Optimizing MLFQ Thresholds using DRL -- 10.5.4.2 Optimizing Long Flows using DRL -- 10.5.4.3 System Overhead -- 10.6 Conclusion and Future Directions -- Bibliography -- Chapter 11 The New Abnormal: Network Anomalies in the AI Era -- 11.1 Introduction -- 11.2 Definitions and Classic Approaches -- 11.2.1 Definitions -- 11.2.2 Anomaly Detection: A Taxonomy -- 11.2.3 Problem Characteristics -- 11.2.4 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| id | DE-604.BV048228649 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:52Z |
| indexdate | 2024-07-10T09:32:33Z |
| institution | BVB |
| isbn | 9781119675440 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033609369 |
| oclc_num | 1266907186 |
| open_access_boolean | |
| owner | DE-573 DE-898 DE-BY-UBR |
| owner_facet | DE-573 DE-898 DE-BY-UBR |
| physical | 1 Online-Ressource (xxxi, 361 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-30-PQE ZDB-35-WEL ZDB-35-WIC FHR_PDA_WIC_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| series2 | IEEE Press Series on Networks and Service Management Ser |
| spelling | Zincir-Heywood, Nur Verfasser aut Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Hoboken, NJ John Wiley & Sons, Incorporated 2021 ©2021 1 Online-Ressource (xxxi, 361 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier IEEE Press Series on Networks and Service Management Ser Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- List of Contributors -- Preface -- Acknowledgments -- Acronyms -- Part I Introduction -- Chapter 1 Overview of Network and Service Management -- 1.1 Network and Service Management at Large -- 1.2 Data Collection and Monitoring Protocols -- 1.2.1 SNMP Protocol Family -- 1.2.2 Syslog Protocol -- 1.2.3 IP Flow Information eXport (IPFIX) -- 1.2.4 IP Performance Metrics (IPPM) -- 1.2.5 Routing Protocols and Monitoring Platforms -- 1.3 Network Configuration Protocol -- 1.3.1 Standard Configuration Protocols and Approaches -- 1.3.2 Proprietary Configuration Protocols -- 1.3.3 Integrated Platforms for Network Monitoring -- 1.4 Novel Solutions and Scenarios -- 1.4.1 Software‐Defined Networking - SDN -- 1.4.2 Network Functions Virtualization - NFV -- Bibliography -- Chapter 2 Overview of Artificial Intelligence and Machine Learning -- 2.1 Overview -- 2.2 Learning Algorithms -- 2.2.1 Supervised Learning -- 2.2.2 Unsupervised Learning -- 2.2.3 Reinforcement Learning -- 2.3 Learning for Network and Service Management -- Bibliography -- Part II Management Models and Frameworks -- Chapter 3 Managing Virtualized Networks and Services with Machine Learning -- 3.1 Introduction -- 3.2 Technology Overview -- 3.2.1 Virtualization of Network Functions -- 3.2.1.1 Resource Partitioning -- 3.2.1.2 Virtualized Network Functions -- 3.2.2 Link Virtualization -- 3.2.2.1 Physical Layer Partitioning -- 3.2.2.2 Virtualization at Higher Layers -- 3.2.3 Network Virtualization -- 3.2.4 Network Slicing -- 3.2.5 Management and Orchestration -- 3.3 State‐of‐the‐Art -- 3.3.1 Network Virtualization -- 3.3.2 Network Functions Virtualization -- 3.3.2.1 Placement -- 3.3.2.2 Scaling -- 3.3.3 Network Slicing -- 3.3.3.1 Admission Control -- 3.3.3.2 Resource Allocation -- 3.4 Conclusion and Future Direction 3.4.1 Intelligent Monitoring -- 3.4.2 Seamless Operation and Maintenance -- 3.4.3 Dynamic Slice Orchestration -- 3.4.4 Automated Failure Management -- 3.4.5 Adaptation and Consolidation of Resources -- 3.4.6 Sensitivity to Heterogeneous Hardware -- 3.4.7 Securing Machine Learning -- Bibliography -- Chapter 4 Self‐Managed 5G Networks1 -- 4.1 Introduction -- 4.2 Technology Overview -- 4.2.1 RAN Virtualization and Management -- 4.2.2 Network Function Virtualization -- 4.2.3 Data Plane Programmability -- 4.2.4 Programmable Optical Switches -- 4.2.5 Network Data Management -- 4.3 5G Management State‐of‐the‐Art -- 4.3.1 RAN resource management -- 4.3.1.1 Context‐Based Clustering and Profiling for User and Network Devices -- 4.3.1.2 Q‐Learning Based RAN Resource Allocation -- 4.3.1.3 vrAIn: AI‐Assisted Resource Orchestration for Virtualized Radio Access Networks -- 4.3.2 Service Orchestration -- 4.3.3 Data Plane Slicing and Programmable Traffic Management -- 4.3.4 Wavelength Allocation -- 4.3.5 Federation -- 4.4 Conclusions and Future Directions -- Bibliography -- Chapter 5 AI in 5G Networks: Challenges and Use Cases -- 5.1 Introduction -- 5.2 Background -- 5.2.1 ML in the Networking Context -- 5.2.2 ML in Virtualized Networks -- 5.2.3 ML for QoE Assessment and Management -- 5.3 Case Studies -- 5.3.1 QoE Estimation and Management -- 5.3.1.1 Main Challenges -- 5.3.1.2 Methodology -- 5.3.1.3 Results and Guidelines -- 5.3.2 Proactive VNF Deployment -- 5.3.2.1 Problem Statement and Main Challenges -- 5.3.2.2 Methodology -- 5.3.2.3 Evaluation Results and Guidelines -- 5.3.3 Multi‐service, Multi‐domain Interconnect -- 5.4 Conclusions and Future Directions -- Bibliography -- Chapter 6 Machine Learning for Resource Allocation in Mobile Broadband Networks -- 6.1 Introduction -- 6.2 ML in Wireless Networks -- 6.2.1 Supervised ML -- 6.2.1.1 Classification Techniques 6.2.1.2 Regression Techniques -- 6.2.2 Unsupervised ML -- 6.2.2.1 Clustering Techniques -- 6.2.2.2 Soft Clustering Techniques -- 6.2.3 Reinforcement Learning -- 6.2.4 Deep Learning -- 6.2.5 Summary -- 6.3 ML‐Enabled Resource Allocation -- 6.3.1 Power Control -- 6.3.1.1 Overview -- 6.3.1.2 State‐of‐the‐Art -- 6.3.1.3 Lessons Learnt -- 6.3.2 Scheduling -- 6.3.2.1 Overview -- 6.3.2.2 State‐of‐the‐Art -- 6.3.2.3 Lessons Learnt -- 6.3.3 User Association -- 6.3.3.1 Overview -- 6.3.3.2 State‐of‐the‐Art -- 6.3.3.3 Lessons Learnt -- 6.3.4 Spectrum Allocation -- 6.3.4.1 Overview -- 6.3.4.2 State‐of‐the‐Art -- 6.3.4.3 Lessons Learnt -- 6.4 Conclusion and Future Directions -- 6.4.1 Transfer Learning -- 6.4.2 Imitation Learning -- 6.4.3 Federated‐Edge Learning -- 6.4.4 Quantum Machine Learning -- Bibliography -- Chapter 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing -- 7.1 Introduction -- 7.2 Technology Overview -- 7.2.1 Fog Computing (FC) -- 7.2.2 Resource Provisioning -- 7.2.3 Service Function Chaining (SFC) -- 7.2.4 Micro‐service Architecture -- 7.2.5 Reinforcement Learning (RL) -- 7.3 State‐of‐the‐Art -- 7.3.1 Resource Allocation for Fog Computing -- 7.3.2 ML Techniques for Resource Allocation -- 7.3.3 RL Methods for Resource Allocation -- 7.4 A RL Approach for SFC Allocation in Fog Computing -- 7.4.1 Problem Formulation -- 7.4.2 Observation Space -- 7.4.3 Action Space -- 7.4.4 Reward Function -- 7.4.5 Agent -- 7.5 Evaluation Setup -- 7.5.1 Fog-Cloud Infrastructure -- 7.5.2 Environment Implementation -- 7.5.3 Environment Configuration -- 7.6 Results -- 7.6.1 Static Scenario -- 7.6.2 Dynamic Scenario -- 7.7 Conclusion and Future Direction -- Bibliography -- Part III Management Functions and Applications -- Chapter 8 Designing Algorithms for Data‐Driven Network Management and Control: State‐of‐the‐Art and Challenges1 8.1 Introduction -- 8.1.1 Contributions -- 8.1.2 Exemplary Network Use Case Study -- 8.2 Technology Overview -- 8.2.1 Data‐Driven Network Optimization -- 8.2.2 Optimization Problems over Graphs -- 8.2.3 From Graphs to ML/AI Input -- 8.2.4 End‐to‐End Learning -- 8.3 Data‐Driven Algorithm Design: State‐of‐the Art -- 8.3.1 Data‐Driven Optimization in General -- 8.3.2 Data‐Driven Network Optimization -- 8.3.3 Non‐graph Related Problems -- 8.4 Future Direction -- 8.4.1 Data Production and Collection -- 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees -- 8.5 Summary -- Acknowledgments -- Bibliography -- Chapter 9 AI‐Driven Performance Management in Data‐Intensive Applications -- 9.1 Introduction -- 9.2 Data‐Processing Frameworks -- 9.2.1 Apache Storm -- 9.2.2 Hadoop MapReduce -- 9.2.3 Apache Spark -- 9.2.4 Apache Flink -- 9.3 State‐of‐the‐Art -- 9.3.1 Optimal Configuration -- 9.3.1.1 Traditional Approaches -- 9.3.1.2 AI Approaches -- 9.3.1.3 Example: AI‐Based Optimal Configuration -- 9.3.2 Performance Anomaly Detection -- 9.3.2.1 Traditional Approaches -- 9.3.2.2 AI Approaches -- 9.3.2.3 Example: ANNs‐Based Anomaly Detection -- 9.3.3 Load Prediction -- 9.3.3.1 Traditional Approaches -- 9.3.3.2 AI Approaches -- 9.3.4 Scaling Techniques -- 9.3.4.1 Traditional Approaches -- 9.3.4.2 AI Approaches -- 9.3.5 Example: RL‐Based Auto‐scaling Policies -- 9.4 Conclusion and Future Direction -- Bibliography -- Chapter 10 Datacenter Traffic Optimization with Deep Reinforcement Learning -- 10.1 Introduction -- 10.2 Technology Overview -- 10.2.1 Deep Reinforcement Learning (DRL) -- 10.2.2 Applying ML to Networks -- 10.2.3 Traffic Optimization Approaches in Datacenter -- 10.2.4 Example: DRL for Flow Scheduling -- 10.2.4.1 Flow Scheduling Problem -- 10.2.4.2 DRL Formulation -- 10.2.4.3 DRL Algorithm -- 10.3 State‐of‐the‐Art: AuTO Design 10.3.1 Problem Identified -- 10.3.2 Overview -- 10.3.3 Peripheral System -- 10.3.3.1 Enforcement Module -- 10.3.3.2 Monitoring Module -- 10.3.4 Central System -- 10.3.5 DRL Formulations and Solutions -- 10.3.5.1 Optimizing MLFQ Thresholds -- 10.3.5.2 Optimizing Long Flows -- 10.4 Implementation -- 10.4.1 Peripheral System -- 10.4.1.1 Monitoring Module (MM): -- 10.4.1.2 Enforcement Module (EM): -- 10.4.2 Central System -- 10.4.2.1 sRLA -- 10.4.2.2 lRLA -- 10.5 Experimental Results -- 10.5.1 Setting -- 10.5.2 Comparison Targets -- 10.5.3 Experiments -- 10.5.3.1 Homogeneous Traffic -- 10.5.3.2 Spatially Heterogeneous Traffic -- 10.5.3.3 Temporally and Spatially Heterogeneous Traffic -- 10.5.4 Deep Dive -- 10.5.4.1 Optimizing MLFQ Thresholds using DRL -- 10.5.4.2 Optimizing Long Flows using DRL -- 10.5.4.3 System Overhead -- 10.6 Conclusion and Future Directions -- Bibliography -- Chapter 11 The New Abnormal: Network Anomalies in the AI Era -- 11.1 Introduction -- 11.2 Definitions and Classic Approaches -- 11.2.1 Definitions -- 11.2.2 Anomaly Detection: A Taxonomy -- 11.2.3 Problem Characteristics -- 11.2.4 Classic Approaches -- 11.3 AI and Anomaly Detection -- 11.3.1 Methodology -- 11.3.2 Deep Neural Networks -- 11.3.3 Representation Learning -- 11.3.4 Autoencoders -- 11.3.5 Generative Adversarial Networks -- 11.3.6 Reinforcement Learning -- 11.3.7 Summary and Takeaways -- 11.4 Technology Overview -- 11.4.1 Production‐Ready Tools -- 11.4.2 Research Alternatives -- 11.4.3 Summary and Takeaways -- 11.5 Conclusions and Future Directions -- Bibliography -- Chapter 12 Automated Orchestration of Security Chains Driven by Process Learning* -- 12.1 Introduction -- 12.2 Related Work -- 12.2.1 Chains of Security Functions -- 12.2.2 Formal Verification of Networking Policies -- 12.3 Background -- 12.3.1 Flow‐Based Detection of Attacks 12.3.2 Programming SDN Controllers Mellia, Marco Sonstige (DE-588)174023359 oth Diao, Yixin 1970- Sonstige (DE-588)1074040627 oth Erscheint auch als Druck-Ausgabe Zincir-Heywood, Nur Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Newark : John Wiley & Sons, Incorporated,c2021 9781119675501 |
| spellingShingle | Zincir-Heywood, Nur Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Cover -- Title Page -- Copyright -- Contents -- Editor Biographies -- List of Contributors -- Preface -- Acknowledgments -- Acronyms -- Part I Introduction -- Chapter 1 Overview of Network and Service Management -- 1.1 Network and Service Management at Large -- 1.2 Data Collection and Monitoring Protocols -- 1.2.1 SNMP Protocol Family -- 1.2.2 Syslog Protocol -- 1.2.3 IP Flow Information eXport (IPFIX) -- 1.2.4 IP Performance Metrics (IPPM) -- 1.2.5 Routing Protocols and Monitoring Platforms -- 1.3 Network Configuration Protocol -- 1.3.1 Standard Configuration Protocols and Approaches -- 1.3.2 Proprietary Configuration Protocols -- 1.3.3 Integrated Platforms for Network Monitoring -- 1.4 Novel Solutions and Scenarios -- 1.4.1 Software‐Defined Networking - SDN -- 1.4.2 Network Functions Virtualization - NFV -- Bibliography -- Chapter 2 Overview of Artificial Intelligence and Machine Learning -- 2.1 Overview -- 2.2 Learning Algorithms -- 2.2.1 Supervised Learning -- 2.2.2 Unsupervised Learning -- 2.2.3 Reinforcement Learning -- 2.3 Learning for Network and Service Management -- Bibliography -- Part II Management Models and Frameworks -- Chapter 3 Managing Virtualized Networks and Services with Machine Learning -- 3.1 Introduction -- 3.2 Technology Overview -- 3.2.1 Virtualization of Network Functions -- 3.2.1.1 Resource Partitioning -- 3.2.1.2 Virtualized Network Functions -- 3.2.2 Link Virtualization -- 3.2.2.1 Physical Layer Partitioning -- 3.2.2.2 Virtualization at Higher Layers -- 3.2.3 Network Virtualization -- 3.2.4 Network Slicing -- 3.2.5 Management and Orchestration -- 3.3 State‐of‐the‐Art -- 3.3.1 Network Virtualization -- 3.3.2 Network Functions Virtualization -- 3.3.2.1 Placement -- 3.3.2.2 Scaling -- 3.3.3 Network Slicing -- 3.3.3.1 Admission Control -- 3.3.3.2 Resource Allocation -- 3.4 Conclusion and Future Direction 3.4.1 Intelligent Monitoring -- 3.4.2 Seamless Operation and Maintenance -- 3.4.3 Dynamic Slice Orchestration -- 3.4.4 Automated Failure Management -- 3.4.5 Adaptation and Consolidation of Resources -- 3.4.6 Sensitivity to Heterogeneous Hardware -- 3.4.7 Securing Machine Learning -- Bibliography -- Chapter 4 Self‐Managed 5G Networks1 -- 4.1 Introduction -- 4.2 Technology Overview -- 4.2.1 RAN Virtualization and Management -- 4.2.2 Network Function Virtualization -- 4.2.3 Data Plane Programmability -- 4.2.4 Programmable Optical Switches -- 4.2.5 Network Data Management -- 4.3 5G Management State‐of‐the‐Art -- 4.3.1 RAN resource management -- 4.3.1.1 Context‐Based Clustering and Profiling for User and Network Devices -- 4.3.1.2 Q‐Learning Based RAN Resource Allocation -- 4.3.1.3 vrAIn: AI‐Assisted Resource Orchestration for Virtualized Radio Access Networks -- 4.3.2 Service Orchestration -- 4.3.3 Data Plane Slicing and Programmable Traffic Management -- 4.3.4 Wavelength Allocation -- 4.3.5 Federation -- 4.4 Conclusions and Future Directions -- Bibliography -- Chapter 5 AI in 5G Networks: Challenges and Use Cases -- 5.1 Introduction -- 5.2 Background -- 5.2.1 ML in the Networking Context -- 5.2.2 ML in Virtualized Networks -- 5.2.3 ML for QoE Assessment and Management -- 5.3 Case Studies -- 5.3.1 QoE Estimation and Management -- 5.3.1.1 Main Challenges -- 5.3.1.2 Methodology -- 5.3.1.3 Results and Guidelines -- 5.3.2 Proactive VNF Deployment -- 5.3.2.1 Problem Statement and Main Challenges -- 5.3.2.2 Methodology -- 5.3.2.3 Evaluation Results and Guidelines -- 5.3.3 Multi‐service, Multi‐domain Interconnect -- 5.4 Conclusions and Future Directions -- Bibliography -- Chapter 6 Machine Learning for Resource Allocation in Mobile Broadband Networks -- 6.1 Introduction -- 6.2 ML in Wireless Networks -- 6.2.1 Supervised ML -- 6.2.1.1 Classification Techniques 6.2.1.2 Regression Techniques -- 6.2.2 Unsupervised ML -- 6.2.2.1 Clustering Techniques -- 6.2.2.2 Soft Clustering Techniques -- 6.2.3 Reinforcement Learning -- 6.2.4 Deep Learning -- 6.2.5 Summary -- 6.3 ML‐Enabled Resource Allocation -- 6.3.1 Power Control -- 6.3.1.1 Overview -- 6.3.1.2 State‐of‐the‐Art -- 6.3.1.3 Lessons Learnt -- 6.3.2 Scheduling -- 6.3.2.1 Overview -- 6.3.2.2 State‐of‐the‐Art -- 6.3.2.3 Lessons Learnt -- 6.3.3 User Association -- 6.3.3.1 Overview -- 6.3.3.2 State‐of‐the‐Art -- 6.3.3.3 Lessons Learnt -- 6.3.4 Spectrum Allocation -- 6.3.4.1 Overview -- 6.3.4.2 State‐of‐the‐Art -- 6.3.4.3 Lessons Learnt -- 6.4 Conclusion and Future Directions -- 6.4.1 Transfer Learning -- 6.4.2 Imitation Learning -- 6.4.3 Federated‐Edge Learning -- 6.4.4 Quantum Machine Learning -- Bibliography -- Chapter 7 Reinforcement Learning for Service Function Chain Allocation in Fog Computing -- 7.1 Introduction -- 7.2 Technology Overview -- 7.2.1 Fog Computing (FC) -- 7.2.2 Resource Provisioning -- 7.2.3 Service Function Chaining (SFC) -- 7.2.4 Micro‐service Architecture -- 7.2.5 Reinforcement Learning (RL) -- 7.3 State‐of‐the‐Art -- 7.3.1 Resource Allocation for Fog Computing -- 7.3.2 ML Techniques for Resource Allocation -- 7.3.3 RL Methods for Resource Allocation -- 7.4 A RL Approach for SFC Allocation in Fog Computing -- 7.4.1 Problem Formulation -- 7.4.2 Observation Space -- 7.4.3 Action Space -- 7.4.4 Reward Function -- 7.4.5 Agent -- 7.5 Evaluation Setup -- 7.5.1 Fog-Cloud Infrastructure -- 7.5.2 Environment Implementation -- 7.5.3 Environment Configuration -- 7.6 Results -- 7.6.1 Static Scenario -- 7.6.2 Dynamic Scenario -- 7.7 Conclusion and Future Direction -- Bibliography -- Part III Management Functions and Applications -- Chapter 8 Designing Algorithms for Data‐Driven Network Management and Control: State‐of‐the‐Art and Challenges1 8.1 Introduction -- 8.1.1 Contributions -- 8.1.2 Exemplary Network Use Case Study -- 8.2 Technology Overview -- 8.2.1 Data‐Driven Network Optimization -- 8.2.2 Optimization Problems over Graphs -- 8.2.3 From Graphs to ML/AI Input -- 8.2.4 End‐to‐End Learning -- 8.3 Data‐Driven Algorithm Design: State‐of‐the Art -- 8.3.1 Data‐Driven Optimization in General -- 8.3.2 Data‐Driven Network Optimization -- 8.3.3 Non‐graph Related Problems -- 8.4 Future Direction -- 8.4.1 Data Production and Collection -- 8.4.2 ML and AI Advanced Algorithms for Network Management with Performance Guarantees -- 8.5 Summary -- Acknowledgments -- Bibliography -- Chapter 9 AI‐Driven Performance Management in Data‐Intensive Applications -- 9.1 Introduction -- 9.2 Data‐Processing Frameworks -- 9.2.1 Apache Storm -- 9.2.2 Hadoop MapReduce -- 9.2.3 Apache Spark -- 9.2.4 Apache Flink -- 9.3 State‐of‐the‐Art -- 9.3.1 Optimal Configuration -- 9.3.1.1 Traditional Approaches -- 9.3.1.2 AI Approaches -- 9.3.1.3 Example: AI‐Based Optimal Configuration -- 9.3.2 Performance Anomaly Detection -- 9.3.2.1 Traditional Approaches -- 9.3.2.2 AI Approaches -- 9.3.2.3 Example: ANNs‐Based Anomaly Detection -- 9.3.3 Load Prediction -- 9.3.3.1 Traditional Approaches -- 9.3.3.2 AI Approaches -- 9.3.4 Scaling Techniques -- 9.3.4.1 Traditional Approaches -- 9.3.4.2 AI Approaches -- 9.3.5 Example: RL‐Based Auto‐scaling Policies -- 9.4 Conclusion and Future Direction -- Bibliography -- Chapter 10 Datacenter Traffic Optimization with Deep Reinforcement Learning -- 10.1 Introduction -- 10.2 Technology Overview -- 10.2.1 Deep Reinforcement Learning (DRL) -- 10.2.2 Applying ML to Networks -- 10.2.3 Traffic Optimization Approaches in Datacenter -- 10.2.4 Example: DRL for Flow Scheduling -- 10.2.4.1 Flow Scheduling Problem -- 10.2.4.2 DRL Formulation -- 10.2.4.3 DRL Algorithm -- 10.3 State‐of‐the‐Art: AuTO Design 10.3.1 Problem Identified -- 10.3.2 Overview -- 10.3.3 Peripheral System -- 10.3.3.1 Enforcement Module -- 10.3.3.2 Monitoring Module -- 10.3.4 Central System -- 10.3.5 DRL Formulations and Solutions -- 10.3.5.1 Optimizing MLFQ Thresholds -- 10.3.5.2 Optimizing Long Flows -- 10.4 Implementation -- 10.4.1 Peripheral System -- 10.4.1.1 Monitoring Module (MM): -- 10.4.1.2 Enforcement Module (EM): -- 10.4.2 Central System -- 10.4.2.1 sRLA -- 10.4.2.2 lRLA -- 10.5 Experimental Results -- 10.5.1 Setting -- 10.5.2 Comparison Targets -- 10.5.3 Experiments -- 10.5.3.1 Homogeneous Traffic -- 10.5.3.2 Spatially Heterogeneous Traffic -- 10.5.3.3 Temporally and Spatially Heterogeneous Traffic -- 10.5.4 Deep Dive -- 10.5.4.1 Optimizing MLFQ Thresholds using DRL -- 10.5.4.2 Optimizing Long Flows using DRL -- 10.5.4.3 System Overhead -- 10.6 Conclusion and Future Directions -- Bibliography -- Chapter 11 The New Abnormal: Network Anomalies in the AI Era -- 11.1 Introduction -- 11.2 Definitions and Classic Approaches -- 11.2.1 Definitions -- 11.2.2 Anomaly Detection: A Taxonomy -- 11.2.3 Problem Characteristics -- 11.2.4 Classic Approaches -- 11.3 AI and Anomaly Detection -- 11.3.1 Methodology -- 11.3.2 Deep Neural Networks -- 11.3.3 Representation Learning -- 11.3.4 Autoencoders -- 11.3.5 Generative Adversarial Networks -- 11.3.6 Reinforcement Learning -- 11.3.7 Summary and Takeaways -- 11.4 Technology Overview -- 11.4.1 Production‐Ready Tools -- 11.4.2 Research Alternatives -- 11.4.3 Summary and Takeaways -- 11.5 Conclusions and Future Directions -- Bibliography -- Chapter 12 Automated Orchestration of Security Chains Driven by Process Learning* -- 12.1 Introduction -- 12.2 Related Work -- 12.2.1 Chains of Security Functions -- 12.2.2 Formal Verification of Networking Policies -- 12.3 Background -- 12.3.1 Flow‐Based Detection of Attacks 12.3.2 Programming SDN Controllers |
| title | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_auth | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_exact_search | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_exact_search_txtP | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_full | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_fullStr | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_full_unstemmed | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_short | Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning |
| title_sort | communication networks and service management in the era of artificial intelligence and machine learning |
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