Admission Control and Virtual Network Embedding in 5G Networks: A Deep Reinforcement-Learning Approach

Sebastian Troia,Ligia Maria Moreira Zorello,Guido Maier,Andres Felipe Rodriguez Vanegas

doi:10.1109/access.2022.3148703

Abstract

Fifth-generation (5G) networks are already available in major urban areas and are expected to bring a major transformation to citizens’ lives. 5G services, such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (mMTC), require a network infrastructure capable of supporting stringent requirements in terms of latency and bandwidth demands; as such, it must be highly dynamic and flexible. Network slicing is a key enabler technology that can provide dynamic and flexible characteristics to 5G network architecture. A network slice (NS) can be defined as a partition of network and IT resources, that is, network links and nodes capacity dedicated to a specific set of service demands. As a result, different NSs can coexist over the same physical infrastructure network and can be used to dynamically and flexibly deploy the aforementioned 5G services. However, to efficiently implement NSs with different requirements, communication service providers (CSPs) that own the physical infrastructure network must adopt sophisticated techniques for admission control and resource allocation of NSs. In this paper, we present a novel framework for admission control and resource allocation of 5G NSs in metro-core networks. Specifically, our framework is based on a deep reinforcement learning (DRL) algorithm called Advantage Actor Critic (A2C), which performs admission control, i.e. it is capable of learning which slice to admit based on the availability of the physical network resources. Then, given the diversity of requirements for each 5G service, we propose different resource allocation algorithms based on integer linear programming (ILP) and heuristics to treat each service accordingly. Results show that our proposed framework can increase the number of admitted NSs with respect to the case in which the admission control is disabled by improving the resource allocation performance.

Highlights

2) We developed the heuristic algorithms by taking into account specific requirements of the 5G Network Slice Requests (NSRs)
The admission control (AC) module is based on the deep reinforcement learning (DRL) A2C algorithm, which is able to select the most profitable NSRs to accommodate onto the substrate network (SN)
integer linear programming (ILP) mathematical formulations) with the aim of embedding each type of 5G NSRs according to their requirements

Summary

INTRODUCTION

Emerging 5G technology is widely available in major urban areas, and coverage is expected to reach less populated areas in the coming years [1]. Slice admission is dictated by the available resources in the network resource pool, and the AC algorithm must consider the available resources in the SN and manage them in order to accommodate as many NSRs as possible Different solutions to this problem have been addressed by several research works by employing various techniques, such as Markov chains [10], big data analytics [11], queuing theory [12], etc. An interesting aspect of DRL is that it implements software agents capable of learning how to optimize an objective function by interacting with an environment that can assume hundreds of thousands of different states For this reason, given the complexity of the AC problem, our goal is to implement a DRL algorithm to optimize the admission of NSRs in a 5G metro-core network environment.

ADMISSION CONTROL TECHNIQUES

PERFORMANCE EVALUATION

RESULTS

COMPLEXITY ANALYSIS

Findings

CONCLUSION