Scalable and Distributed Cloud Continuum Orchestration for Next-Generation Applications
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 135
Special Issue Editors
Interests: control theory; cloud computing; cyber–physical systems; industry 4.0
Special Issues, Collections and Topics in MDPI journals
Interests: computer networking; network optimisation; cloud computing; edge computing; multi-criteria decision-making; dynamic resource allocation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the advent of the Internet of 5G, Industry 4.0, and AI, the cloud continuum provides the computing and networking resources for the deployment of modern services in various domains, such as smart manufacturing and the AI sector. Additionally, the vast amount of data must be processed and delivered for training and optimizing complex processes beyond the traditional network services and applications.
Such complex systems include heterogeneous cloud infrastructure, mobile robots, manufacturing execution systems, and logistics. The orchestration of these systems requires the coordination of versatile resources with complex and competitive requirements and constraints.
Furthermore, the emergence of the AI era enables the development of many data-driven applications that are integrated into each step of the processes and production. The management of large volumes of data is crucial for the successful training and performance of these AI-based applications.
With this context in mind, this Special Issue invites novel conceptual, theoretical, and experimental contributions addressing the unresolved challenges in the area of the cloud continuum, AI, and smart manufacturing. The topics of interest include, but are not limited to, the following:
- Resource allocation and scheduling in the cloud continuum;
- Virtual network embedding in the cloud continuum;
- Scalability issues in the cloud continuum;
- Novel architectures in the cloud continuum;
- AI for QoS management in cloud continuum.
- Task offloading in the cloud continuum;
- Energy sustainability in the cloud continuum;
- Digital twins in the cloud continuum;
- Network/resource monitoring in the cloud continuum;
- Computation and network infrastructure resilience and reliability;
- Virtualization solutions for distributed application deployment in the cloud continuum;
- Security issues in the cloud continuum;
- Intent-based networking;
- Data analytics, traffic analysis, and classification in the cloud continuum;
- Real-time scheduling for LLM inference;
- Advanced AI techniques for smart manufacturing;
- Graph neural networks for manufacturing as a service;
- Testbeds and experimental facility reports.
Dr. Dimitrios Dechouniotis
Dr. Ioannis Dimolitsas
Dr. Dimitrios Spatharakis
Guest Editors
Manuscript Submission Information
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Keywords
- AI
- cloud computing
- edge computing
- cyber–physical systems
- smart manufacturing
- digital twins
- LLM inference
- distributed service embedding
- resource orchestration
- cloud manufacturing
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