Towards personalized data-driven bundle design with QoS constraint

Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiativ

Radio resource sharing and edge caching with latency constraint for local 5G operator:geometric programming meets Stackelberg game

Abstract We develop a novel game-theoretic framework with geometric programming to model and analyze cache-enabled base stations (BSs) with infrastructure sharing for local 5G operator (OP) networks. In such a network, the local 5G OP provides wireless network in indoor area and rents out the infrastructure which are RAN and cache storage to multiple mobile network operators (MNOs) while guarantee the quality-of-experience (QoE) at the users (UEs) of MNOs. We formulate a Stackelberg game model where the local 5G OP is the leader and the MNOs are the followers. The local 5G OP aims to maximize its profit by optimizing its infrastructure rental fee, and the MNOs aim to minimize their renting cost of infrastructure by minimizing the “cache intensity” subject to latency constraint at each UE. The optimization problems of the local 5G OP and the MNOs are transformed into geometric programming. Accordingly, the Stackelberg equilibrium is obtained through the succesive geometric programming method. Since the MNOs share their rented infrastructure, for cost sharing, we apply the concept of Shapley value to divide the cost among the MNOs. Finally, we present an extensive performance evaluation that reveals interesting insights into designing resource sharing with edge caching in local 5G OP networks

Network slicing with mobile edge computing for micro-operator networks in beyond 5G

Abstract We model the scenarios of network slicing allocation for the micro-operator (MO) network. The MO creates the slices “as a service” of wireless resource and then allocates these slices to multiple mobile network operators (MNOs). We propose the slice allocation problem of multiple MNOs with the goal of maximizing the social welfare of the network defined as sum rate of all MNOs. The many-to-one matching game framework is adopted to solve this problem. Then, the generic Markov Chain Monte Carlo (MCMC) method is introduced for the computation of game theoretical solution. After the MNOs obtain the slices, for each small cell base station (SBS), we investigate the role of power allocation using Q-learning and uniform power. We numerically show that the solution of the matching game leads to two-sided stable matching. Furthermore, for each MNO, we explore the problem of infrastructure cost minimization constrained on the latency at the user equipment (UE). The optimal solution is given by a greedy fractional knapsack algorithm. We illustrate that it is sufficient for the MNO to use a small fraction of the SBS to serve the UE while satisfying the latency constraint. For the problem of overall data rate maximization, we numerically show that the power allocation has significant effect on the social welfare of the system

Resource virtualization with edge caching and latency constraint for local B5G operator

Abstract The rapidly increasing demand in indoor smallcell networks has given rise to the concept of local beyond 5G (B5G) operator (OP) for local service delivery. The local B5G OP aims to provide wireless network using licensed subbands in an indoor area and tries to gain profits by renting out the infrastructure to the mobile network operators (MNOs). With local B5G OP deployment, the quality of service (QoS) can be guaranteed at mobile broadband users (UEs) and smart devices, i.e., machine type communications (MTC) and ultra reliable low latency (uRLLC). In this paper, we consider the scenario that the local B5G OP aims to maximize profit by optimizing its infrastructure rental fee while renting out cache-enabled smallcell base stations (SBSs) to the MNOs. Each MNO tries to minimize the cache intensity subject to latency constraint at mobile UE. The concept of infrastructure sharing is also deployed at the local B5G OP such that multiple MNOs can utilize the same cache-enabled SBSs simultaneously and the local B5G OP will cache the popular files according to the MNO’s largest demand. The optimization problems of the local B5G OP and the MNOs can be transformed into geometric programming problems. Then, we show that the Stackelberg equilibrium is obtained through successive geometric programming (SGP) method. Lastly, we perform an extensive performance evaluation that reveals interesting insights including the optimal SBS intensity that MNOs should rent from the local B5G OP as to satisfy end-to-end latency, 10 -3 sec, of data transmission from each SBS to UE. The optimal price of renting out infrastructure for the local B5G OP at the Stackelberg equilibrium is also illustrated

Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching

Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and

Distributed Resource Allocation for Data Center Networks: A Hierarchical Game Approach

The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the DCOs and the SSs