Efficient and Fair Multi-Resource Allocation in Dynamic Fog Radio Access Network Slicing

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: Paretooptimality, 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.

Slice Sandwich: Jagged Slicing Multi-Tier Dynamic Resources for Diversified V2X Services

“…To address the multi-dimensional resources allocation issue, Mohammed et al proposed a multi-dimensional resources slicing scheme [50]. Both the InP and SPs adopt the dominant resource fairness (DRF) approach to allocate multi-dimensional resources.…”

“…• Hierarchical resource allocation schemes: The twotimescale bidding resource management scheme (2Ts-BRMS) adopts the generalized Kelly mechanism (GKM) to address the inter-slice multi-dimensional resource configuration problem and allocates resources to users according to channel quality (CQ) [23]. In the two-timescale fair resource management scheme (2Ts-FRMS), both the InP and SPs adopt the dominant resource fairness (DRF) approach to allocate multi-dimensional resources [50].…”