A Resoure Allocation Framework for Network Slicing with Multi-service Coexistence

Network slicing has been widely recognized as the architectural technology for 5G and beyond wireless network systems to provide tailored service for diverse applications by flexibly splitting and allocating various heterogeneous resources. However, it is still challenging to meet the strict delay requirements of a large number of delay-sensitive applications under traditional slicing architectures. One potential way to tackle this issue is to build network slicing upon Mobile Edge Computing (MEC) systems, where both communication and computing resources are integrated for providing customized service. As such, in this paper, we propose a framework, to jointly optimize communication and computing resources under the scenario of multi-service coexistence, with the objective to minimize the system cost while meeting the diverse QoS requirements. To make the original optimization problem more tractable, we decompose it into two convex sub-problems first. Then we obtain the optimal solutions of the two sub-problems respectively, and finally derive the optimal communication and computing resource allocation scheme based on the optimal solutions of these two sub-problems. Simulation results show that our proposed scheme significantly saves the system cost under various scenarios compared with other benchmarks

A two-timescale approach for network slicing in C-RAN.

Network slicing is a promising technique for cloud radio access networks (C-RANs). It enables multiple tenants (i.e., service providers) to reserve resources from an infrastructure provider. However, users' mobility and traffic variation result in resource demand uncertainty for resource reservation. Meanwhile, the inaccurate channel state information (CSI) estimation may lead to difficulties in guaranteeing the quality of service (QoS). To this end, we propose a two-timescale resource management scheme for network slicing in C-RAN, aiming at maximizing the profit of a tenant, which is the difference between the revenue from its subscribers and the resource reservation cost. The proposed scheme is under a hierarchical control architecture which includes long timescale resource reservation for a slice and short timescale intra-slice resource allocation. To handle traffic variation, we utilize the statistics of users' traffic. Moreover, to guarantee the QoS under CSI uncertainty, we apply the uncertainty set of CSI for resource allocation among users. We formulate the profit maximization as a two-stage stochastic programming problem. In this problem, long timescale resource reservation for a slice is performed in the first stage with only the statistical knowledge of users' traffic. Given the decision in the first stage, short timescale intra-slice resource allocation is performed in the second stage, which is adaptive to real-time user arrival and departure. To solve the problem, we first transform the stochastic programming problem into a deterministic optimization problem. We then introduce a maximum interference constraint and transform the QoS constraint under CSI uncertainty into linear matrix inequalities. We further apply semidefinite relaxation to transform the problem into a mixed integer nonconvex optimization problem, which can be solved by combining branch-and-bound and primal-relaxed dual techniques. Simulation results show that our proposed scheme can well adapt to traffic variation and CSI uncertainty. It obtains a higher profit when compared with several baseline schemes.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat