Placement and Assignment of Servers in Virtualized Radio Access Networks

TL;DR

This paper addresses the problem of optimally placing and assigning servers in Virtualized Radio Access Networks (VRANs) to minimize costs and latency, proposing both an exact formulation and a greedy approximation.

Contribution

It formally defines the VRAN Placement and Assignment Problem (VRAN-PAP), formulates it as a Binary Integer Linear Program, and introduces a greedy approximation for larger instances.

Findings

The BILP effectively minimizes costs and latency for small networks.

The greedy algorithm provides near-optimal solutions with reduced computation time.

Simulation results compare the performance of both algorithms across various network scenarios.

Abstract

The virtualization of Radio Access Networks (RANs) has been proposed as one of the important use cases of Network Function Virtualization (NFV). In Virtualized Radio Access Networks (VRANs), some functions from a Base Station (BS), such as those which make up the Base Band Unit (BBU), may be implemented in a shared infrastructure located at either a data center or distributed in network nodes. For the latter option, one challenge is in deciding which subset of the available network nodes can be used to host the physical BBU servers (the placement problem), and then to which of the available physical BBUs each Remote Radio Head (RRH) should be assigned (the assignment problem). These two problems constitute what we refer to as the VRAN Placement and Assignment Problem (VRAN-PAP). In this paper, we start by formally defining the VRAN-PAP before formulating it as a Binary Integer Linear Program (BILP) whose objective is to minimize the server and front haul link setup costs as well as the latency between each RRH and its assigned BBU. Since the BILP could become computationally intractable, we also propose a greedy approximation for larger instances of the VRAN-PAP. We perform simulations to compare both algorithms in terms of solution quality as well as computation time under varying network sizes and setup budgets.