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SCOPE
As networks and services grow increasingly complex and dynamic, the need for efficient and intelligent management solutions becomes paramount. Automated Machine Learning (AutoML) has emerged as a transformative technology with the potential to revolutionize network and service management by enabling ”zero-touch” operations. AutoML leverages Artificial Intelligence (AI) and Machine Learning (ML) to automate the design, deployment, and optimization of network and service configurations, leading to enhanced performance, reduced downtime, and improved user experiences. The primary focus is on the application of AutoML in the context of 5G, 6G, and other advanced technologies. This workshop encompasses a broad spectrum of topics, including the use of AutoML to automate the design, deployment, and optimization of network and service configurations, resulting in enhanced performance, reduced downtime, and improved user experiences. It explores the concept of zero-touch operations, delving into the benefits and real-world implementations of this approach. Integrating AutoML into the 5G and 6G landscape, security implications, and service orchestration are key discussion areas. Additionally, the workshop addresses the challenges and opportunities of AutoML in cross-domain, multi-vendor environments. It encourages the sharing of best practices, case studies, and future research directions to advance the field.
TOPICS OF INTEREST:
We invite researchers and practitioners to submit their latest research and innovative solutions in the area of Automated Machine Learning (AutoML) for Zero-touch Network and Service Management. Topics of interest include, but are not limited to:
- Zero-touch provisioning and deployment of network services
- Self-healing and self-optimizing networks
- Predictive maintenance and fault detection
- Real-time network performance monitoring
- Network automation and orchestration
- Data-driven approaches for network and service management
- AI-driven analytics and insights for network operations
- Security and reliability in AutoML-enabled networks
- Cross-domain and Cross-layer Integration
- Edge computing and AutoML
- Sustainability and energy efficiency
- AI in Network Slicing
- Federated learning and reinforcement learning for intelligent communications
- Intelligent network slicing techniques
- RAN configuration optimization
- Case studies and real-world applications of AutoML in network management
IMPORTANT DATES:
- Paper Submission: 22 December 2023
- Acceptance Notification: 15 January 2024
- Camera Ready and Registration: 25 January 2024
WORKSHOP GENERAL CO-CHAIRS:
- Latif Ladid, University of Luxembourg, Luxembourg
- Mohammed Ahmed, Canada National Railways, Canada
- Kuljeet Kaur, University of Quebec, Canada
- Baek-Young Choi, University of Missouri, USA
- Toktam Mahmoodi, King’s College London, United Kingdom (UK)
- Kapal Dev, Munster Technological University, Ireland
- Mostafa Korashy, University of Quebec, Canada
EDAS SUBMISSION:
- EDAS Submission link: https://edas.info/N31860
