On Modeling Network Slicing Communication Resources with SARSA Optimization

TL;DR

This paper introduces a conceptual model for network slicing resource optimization using SARSA reinforcement learning, addressing resource sharing among communication slices in 5G/6G and IoT networks.

Contribution

It presents a novel conceptual model and formulates an optimization problem for communication resource sharing, solved with a SARSA agent and demonstrated through a prototype.

Findings

SARSA effectively optimizes resource sharing in network slices

Prototype implementation validates the proposed approach

Results show improved resource allocation efficiency

Abstract

Network slicing is a crucial enabler to support the composition and deployment of virtual network infrastructures required by the dynamic behavior of networks like 5G/6G mobile networks, IoT-aware networks, e-health systems, and industry verticals like the internet of vehicles (IoV) and industry 4.0. The communication slices and their allocated communication resources are essential in slicing architectures for resource orchestration and allocation, virtual network function (VNF) deployment, and slice operation functionalities. The communication slices provide the communications capabilities required to support slice operation, SLA guarantees, and QoS/ QoE application requirements. Therefore, this contribution proposes a networking slicing conceptual model to formulate the optimization problem related to the sharing of communication resources among communication slices. First, we present a conceptual model of network slicing, we then formulate analytically some aspects of the model and the optimization problem to address. Next, we proposed to use a SARSA agent to solve the problem and implement a proof of concept prototype. Finally, we present the obtained results and discuss them.