Online Estimation of Resource Overload Risk in 5G Multi-Tenancy Network

TL;DR

This paper addresses the challenge of accurately estimating resource overload risk in 5G network slicing to ensure reliable multi-tenant services, considering complex network dynamics and resource overbooking.

Contribution

It proposes an online estimation method for resource overload risk in 5G multi-tenancy networks, improving SLA compliance and network reliability.

Findings

Effective risk estimation improves SLA adherence.

The method adapts to network dynamics in real-time.

Enhanced reliability of Slice-as-a-Service deployments.

Abstract

The technology of network slicing, as the most characteristic feature of the fifth generation (5G) wireless networks, manages the resources and network functions in heterogeneous and logically isolated slices on the top of a shared physical infrastructure, where every slice can be independently customized to fulfill the specific requirements of its devoted service type. It enables a new paradigm of multi-tenancy networking, where the network slices can be leased by the mobile network operator (MNO) to tenants in form of public cloud computing service, known as Slice-asa- Service (SlaaS). Similar to classical cloud computing scenarios, SlaaS benefits from overbooking its resources to numerous tenants, taking advantage of the resource elasticity and diversity, at a price of risking overloading network resources and violating the service-level agreements (SLAs), which stipulate the quality of service (QoS) that shall be guaranteed to the network slices. Thus, it becomes a critical challenge to the MNOs, accurately estimating the resource overload risk - especially under the sophisticated network dynamics - for monitoring and enhancing the reliability of SlaaS business.