Smart Service-Oriented Clustering for Dynamic Slice Configuration

TL;DR

This paper introduces SOCL, a framework that uses machine learning clustering techniques to analyze user service consumption patterns in 5G networks, aiding dynamic slice configuration.

Contribution

It presents a novel framework combining network simulation and clustering algorithms for analyzing user behavior in 5G network slicing environments.

Findings

Effective clustering of user service consumption patterns

Enhanced understanding of service demand for better slice management

Framework benefits demonstrated through simulation results

Abstract

The fifth generation (5G) and beyond wireless networks are foreseen to operate in a fully automated manner, in order to fulfill the promise of ultra-short latency, meet the exponentially increasing resource requirements, and offer the quality of experience (QoE) expected from end-users. Among the ingredients involved in such environments, network slicing enables the creation of logical networks tailored to support specific application demands (i.e., service level agreement SLA, quality of service QoS, etc.) on top of physical infrastructure. This creates the need for mechanisms that can collect spatiotemporal information on users'service consumption, and identify meaningful insights and patterns, leveraging machinelearning techniques. In this vein, our paper proposes a framework dubbed"SOCLfor" the Service Oriented CLustering, analysis and profiling of users (i.e., humans, sensors, etc.) when consuming enhanced Mobile BroadBand (eMBB) applications, internet of things (IoT) services, and unmanned aerial vehicles services (UAVs). SOCL relies mainly on the realistic network simulation framework"network slice planne"(NSP), and two clustering methods namely K-means and hierarchical clustering. The obtained results showcase interesting features, highlighting the benefit of the proposed framework.