Strategic Resource Management in 5G Network Slicing. (Invited paper)

International audienceNetwork Slicing is one of the essential concepts that has been introduced in 5G networks design to support demand expressed by next generation services. Network slicing will also bring new business opportunities for service providers (SPs) and virtual network operators, allowing them to run their virtual, independent business operations on shared physical infrastructure. We consider a marketplace where service providers (SPs) i.e., slice tenants lease the resources from an infrastructure provider (InP) through a network slicing mechanism. They compete to offer a certain communication service to end-users. We show that the competition between SPs can be model using the multiresource Tullock contest (TC) framework, where SPs exert effort by expending costly resource to attract users. We study the competition between the SPs under a static and dynamic resource sharing scheme. In a dynamic resource sharing scheme, SPs are pre-assigned with fixed shares (budgets) of infrastructure, and they are allowed to redistribute their shares and customise their allocation to maximise their profit. The decision problem of SPs is analysed using non-cooperative game theory, and it is shown that the resultant game admits a unique Nash Equilibrium (NE). Furthermore, a distributed reinforcement algorithm is proposed that allows each SP to reach the game's unique Nash equilibrium. Finally, simulations results are conducted to analyse the interaction between market players and the economic efficacy of the network sharing mechanism

Routing into parallel collision channels

International audienceWe study a Medium Access game modeled as a splittable atomic routing game in a parallel link topology. Each player has to decide how to split her traffic among the links. We take the expected loss probability of a player as her cost and consider various loss scenarios: 1) the M/M/1/1 queue in which an arrival that finds another packet in service is lost, 2) Losses occur as in 1, except that if arrival occurs when another packet is served, then both are lost. We furthermore assume that the packet in service is aborted if there was a collision. 3) Like 2, but the packet in service is not aborted. We study the existence and uniqueness of equilibrium under these three types of losses

Maximizing amount of transferred traffic for battery powered mobiles

International audienceThere is a fast growing demand for mobile telephones. These rely on batteries to provide the power needed for transmission and for reception (up and downlink communications). Considering uplink, we analyse how the characteristics of the battery affect the amount of information that one can draw out from the terminal. We focus in particular on the impact of the charge in the battery on the internal resistance which grows as the battery depletes

Balancing Efficiency and Privacy in a Decision-Dependent Network Game

We consider a network game, where End Users (EUs) minimize their cost by computing their demand and generation while satisfying a set of local and coupling constraints. Their nominal demand constitutes sensitive information, that they might want to keep private. We prove that the network game admits a unique Variational Equilibrium, which depends on the private information of all the EUs. A data aggregator is introduced, which aims to learn the EUs' private information. The EUs might have incentives to report biased and noisy readings to preserve their privacy, which creates shifts in their strategies. Relying on performative prediction, we define a decision-dependent game G^stoch to couple the network game with a data market. Two variants of the Repeated Stochastic Gradient Method (RSGM) are proposed to compute the Performatively Stable Equilibrium solution of G^stoch , that outperform RSGM with respect to efficiency gap minimization, privacy preservation, and convergence rates in numerical simulations

Balancing Efficiency and Privacy in a Decision-Dependent Network Game

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Multi Resource Allocation for Network Slices with Multi-Level fairness

International audienceNetwork slicing is becoming the platform of choice for several applications and services. Nowadays most applications are virtualized to gain flexibility and portability. With network slicing, operators can create multiple network slices or tenants, which can be used for certain applications with specific requirements. Behind the network slicing, a slice expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. Resource allocation decision encompasses a combination of different resource types (e.g., radio resource, CPU, memory, bandwidth). In this paper, we explore a differential pricing scheme that maximizes social welfare among slices as well as among end-users. To do so, we propose a pricing mechanism that makes fairness at multiple levels: fairness among slices and fairness among slice locations supported by each slice. Therefore, the proposed scheme is beneficial for both the slices and the end-users independent of their location. Additionally, we study the case where slices can manipulate their preferences to improve their utility. We show that the Fisher market game always has a pure Nash equilibrium and we prove Price of Anarchy is 1 N , where N is the number of slices. Finally, we conduct simulations using Amazon EC2 instances to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model

The Mask Game with Multiple Populations

International audienceMasks save lives. Therefore, whilst the culture of wearing masks is promoted, it is critical to understand the various aspects of how that culture is adopted. The main contribution of this paper is in the modeling of the mask game. Wearing a mask provides partial protection against epidemics at some cost of comfort. Players can be differentiated according to both their risk-state as well as their health-state (susceptible, infected and removed). We formulate the problem as a Bayesian game in which players know their own risk-state and ignore their own health state and the health and risk-states of their counterparts. Using ideas from evolutionary games, we reduce the problem to a one-shot equivalent game and describe the structure of the symmetric equilibria. We prove that the policies adopted by the players at such equilibria admit a threshold structure. More specifically, players wear masks only if their risk-state is equal to or bigger than a given threshold