Projects per year
Abstract
Future wireless networks should meet heterogeneous service requirements of diverse applications, including interactive multimedia, augmented reality, and autonomous driving. The fog radio access network (Fog-RAN) is a novel architecture that enables efficient and flexible allocation of network resources to end users. However, guaranteeing application-specific service requirements while maximizing resource utilization is an open challenge in Fog-RANs. This article proposes a multi-resource Fog-RAN slicing scheme that maximizes network resource utilization and satisfies important economic properties: Pareto optimality, envy-freeness, and sharing incentive. The proposed solution considers both heterogeneous resources (i.e., bandwidth, storage and computing) and the different service levels defined in 5G networks. Accordingly, a two-level resource scheduling mechanism is devised to jointly allocate Fog-RAN resources to slices in two stages: a broker allocates resources to slices at fog nodes over a given time window; a slice hypervisor then allocates slice-specific resources at each fog node to users with a much shorter time scale. An extensive evaluation based on real-world datasets demonstrates that the proposed solution significantly increases the monetary gain of service providers, namely, by 32% to 60% compared to the state of the art, including dynamic hierarchical resource allocation and dynamic slicing with proportional allocation.
Original language | English |
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Pages (from-to) | 24600-24614 |
Number of pages | 15 |
Journal | IEEE Internet of Things Journal |
Volume | 9 |
Issue number | 24 |
Early online date | 20 Jul 2022 |
DOIs | |
Publication status | Published - 15 Dec 2022 |
MoE publication type | A1 Journal article-refereed |
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MeXICO: Mobile Cross Reality through Immersive Computing
Di Francesco, M., Corneo, L., Premsankar, G., Vaishnav, A., Montoya Freire, M. & Khatri, A.
01/09/2020 → 31/08/2024
Project: Academy of Finland: Other research funding
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Mission-Critical Internet of Things Applications over Fog Networks
Di Francesco, M., Corneo, L., Kortoci, P., Ranjbaran, S., Jedari Ghourichaei, B., Montoya Freire, M. & Premsankar, G.
01/01/2019 → 31/12/2021
Project: Academy of Finland: Other research funding