DeepFloat: Resource-Efficient DynamicManagement of Vehicular Floating Content
| Art der Publikation: | Artikel |
| Zitat: | |
| Zeitschrift: | ITC 31- Networked Systems and Services |
| Jahr: | 2019 |
| Monat: | August |
| Abriss: | Opportunistic communications are expected to playa crucial role in enabling context-aware vehicular services. Awidely investigated opportunistic communication paradigm forstoring a piece of content probabilistically in a geographicalarea is Floating Content (FC). A key issue in the practicaldeployment of FC is how to tune content replication and cachingin a way which achieves a target performance (in terms ofthe mean fraction of users possessing the content in a givenregion of space) while minimizing the use of bandwidth andhost memory. Fully distributed, distance-based approaches provehighly inefficient, and may not meet the performance target,while centralized, model-based approaches do not perform wellin realistic, inhomogeneous settings.In this work, we present a data-driven centralized approachto resource-efficient, QoS-aware dynamic management of FC.We propose a Deep Learning strategy, which employs a Con-volutional Neural Network (CNN) to capture the relationshipsbetween patterns of users mobility, of content diffusion andreplication, and FC performance in terms of resource utilizationand of content availability within a given area. Numericalevaluations show the effectiveness of our approach in derivingstrategies which efficiently modulate the FC operation in spaceand effectively adapt to mobility pattern changes over time |
| Nutzerfelder: | Networking VANET Floating Content Machine Learning |
| Schlagworte: | |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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