In the context of team Cornet’s seminars, Mandar Datar (LIA) will present his research work on Fisher market model-based resource allocation for 5G network slicing, on June 6, 2022, at 11:35 in the meeting room.
Abstract: Network slicing is one of the potential technologies to support a higher degree of heterogeneity and flexibility required by next-generation services in 5G networks. In a 5G network, slicing is a specific form of virtualization that allows multiple logical networks (e.g., Mobile Virtual Network Operators (MVNOs)) to run on top of shared physical infrastructure. In the multi-resource allocation scheme, a set of heterogeneous resources (e.g., radio resource, CPU, memory, bandwidth) is shared among Slice tenants or MVNOs, and a portion of them is allocated to each MVNO to support dedicated service to their customers. We consider a scenario where service providers or slice tenants need heterogeneous resources at geographically distributed locations to support the service for their end-users. We propose a resource sharing scheme based on the Fisher market model and the Trading post mechanism. In the proposed scheme, each slice owns the budget representing its infrastructure share or purchasing power in the market. The slices acquire different resources by spending their budgets to offer the service to different classes of users, which are classified according to their service needs and priorities. We assume that service providers employ the well know α fairness criteria while delivering the service to their subscribers. The proposed allocation scheme aims to find a market equilibrium that provides allocation and resource pricing where each slice is satisfied with allocation and resources to be fully utilized. We show that the market equilibrium solution problem can be formulated as a convex optimization problem whose primal and dual optimal solution provides equilibrium allocation and pricing. We build a decentralized algorithm based on a convex optimization problem and potential function technique and proportional sharing rule that enables service providers to reach the market equilibrium in a decentralized fashion. We theoretically evaluate the proposed allocation scheme’s performance by comparing it with the Social Optimal and Static Proportional allocation schemes. Finally, we run numerical simulations to analyse the fairness and efficiency properties of the proposed scheme.