NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium

Akkaya, K [szerk.]; Festor, O [szerk.]; Fung, C [szerk.]; Rahman, M A [szerk.]; Granville, L Z [szerk.]; dos Santos, C R A [szerk.]

Angol nyelvű Konferenciakötet (Könyv) Tudományos

Megjelent: Institute of Electrical and Electronics Engineers (IEEE), Piscataway (NJ), Amerikai Egyesült Államok 2023

Konferencia: 2023 IEEE/IFIP Network Operations and Management Symposium, NOMS 2023 2023-05-08 [Miami (FL), Amerikai Egyesült Államok]

Azonosítók

MTMT: 34033293
DOI: 10.1109/NOMS56928.2023
ISBN: 9781665477161

Fejezetek

Balogh Marcell et al. Digital Twins in Industry 5.0: Challenges in Modeling and Communication. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Camacho J. et al. Quality In / Quality Out: Data quality more relevant than model choice in anomaly detection with the UGR'16. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-5
Czentye János et al. Cost-optimal Operation of Latency Constrained Serverless Applications: From Theory to Practice. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-10
Dietz Katharina et al. Moving Down the Stack: Performance Evaluation of Packet Processing Technologies for Stateful Firewalls. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-7
Fukuda N. et al. Optimizing Resource Placement for Long-term Disaster Response. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-5
Hegedus C. et al. Enabling Scalable Smart Vertical Farming with IoT and Machine Learning Technologies. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-4
Hsu C.S.-H. et al. V2N Service Scaling with Deep Reinforcement Learning. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-5
Javed S. et al. A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud-based Collaborative Learning at the Edge. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Leiter Ákos et al. GitOps and Kubernetes Operator-based Network Function Configuration. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-5
Makai Lajos Bence et al. Predicting Mobility Management Demands of Cellular Networks based on User Behavior. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Nguyen Huu Nghia et al. A Comprehensive P4-based Monitoring Framework for L4S leveraging In-band Network Telemetry. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Nunes D.C. et al. Serene: Handling the Effects of Stragglers in In-Network Machine Learning Aggregation. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-10
Pathak Divya et al. Accelerating PUF-based Authentication Protocols Using Programmable Switch. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-10
Pelle István et al. A Comprehensive Performance Analysis of Stream Processing with Kafka in Cloud Native Deployments for IoT Use-cases. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Revisnyei Péter et al. Performance of a TDOA indoor positioning solution in real-world 5G network. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Russell Patrick et al. On the Fence: Anomaly Detection in IoT Networks. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-4
Santos J. et al. gym-hpa: Efficient Auto-Scaling via Reinforcement Learning for Complex Microservice-based Applications in Kubernetes. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-9
Sharara Mahdi et al. Reinforcement Learning based model for Maximizing Operator's Profit in Open-RAN. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-5
Somogyi Á et al. Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Sonkoly Balázs et al. Towards an Edge Cloud Based Coordination Platform for Multi-User AR Applications Built on Open-Source SLAMs. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Szabó G. et al. Impact of Network Resource Management On Quality of Industrial Processes. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-4
Toka László et al. 5G on the roads: optimizing the latency of federated analysis in vehicular edge networks. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-5
Toka László et al. Federated learning for vehicular coordination use cases. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6
Varga Pal Patrik et al. Analyzing self-similarities in network traffic, water levels, and currency exchange rates. (2023) Megjelent: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium pp. 1-6

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NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium

Akkaya, K [szerk.]; Festor, O [szerk.]; Fung, C [szerk.]; Rahman, M A [szerk.]; Granville, L Z [szerk.]; dos Santos, C R A [szerk.]

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