A5057533274- Cloud Computing and Resource Management
- IoT and Edge/Fog Computing
- Context-Aware Activity Recognition Systems
- Software-Defined Networks and 5G
- Software System Performance and Reliability
- Advanced Wireless Communication Technologies
- Smart Grid Energy Management
- Age of Information Optimization
- Blockchain Technology Applications and Security
University of Bologna
2022-2024
Laboratori Guglielmo Marconi (Italy)
2023-2024
The Edge Computing paradigm is increasingly gaining traction in modern telecommunication scenarios, as it enables the offloading of computational tasks from end devices to a variety nodes located close proximity them. This approach essential for meeting ever-stricter Quality Service requirements imposed by applications. Concurrently, advent Data Plane Programmability allows unmatched flexibility on networking plane, supporting processing multiple protocols logically centralized fashion with...
We consider the problem of designing and training a neural network-based orchestrator for fog computing service deployment. Our goal is to train an able optimize diversified competing QoS requirements, such as blocking probability delay, while potentially supporting thousands nodes. To cope with said challenges, we implement our Deep Set (DS) network operating on sets nodes, leverage Reinforcement Learning (DRL) invalid action masking find optimal trade-off between objectives. Illustrative...
With the establishment of Everything-as-a-Service (XaaS) paradigm for service provisioning, coupled with increasingly-demanding requirements imposed by modern network services, need a XaaS-aware orchestration system able to cope heterogeneous infrastructure, such as one Fog Computing environments, is evident. In this work, we describe working principles and implementation aspects that allow services offered according Function-as-a-Service (FaaS) model. The live demonstration will showcase...
Fog computing is a distributed paradigm that extends cloud closer to the edge of network, and even beyond that. By employing local resources, it enables quicker more effective data processing analysis. The optimization automation resource allocation, processing, job scheduling in fog environment are made possible by application machine learning Computing Orchestration. It also important, when working with network models, consider XaaS paradigm, as promotes flexibility scalability services,...
End-to-end service orchestration leveraging computing and communication resources in the cloud continuum is addressed. A modular architecture proposed to facilitate data-driven/AI adoption, while achieving enhanced energy efficient automation 6G provisioning. selection of available enabling technologies deploy presented.