Open Access

ARTICLE

An Intent-Driven Closed-Loop Platform for 5G Network Service Orchestration

by Talha Ahmed Khan, Khizar Abbas, Afaq Muhammad, Wang-Cheol Song^*

Department of Computer Engineering, Jeju National University, Jeju, Korea

* Corresponding Author: Wang-Cheol Song. Email:

(This article belongs to the Special Issue: Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks)

Computers, Materials & Continua 2022, 70(3), 4323-4340. https://doi.org/10.32604/cmc.2022.017118

Received 21 January 2021; Accepted 15 July 2021; Issue published 11 October 2021

Abstract

The scope of the 5G network is not only limited to the enhancements in the form of the quality of service (QoS), but it also includes a wide range of services with various requirements. Besides this, many approaches and platforms are under the umbrella of 5G to achieve the goals of end-to-end service provisioning. However, the management of multiple services over heterogeneous platforms is a complex task. Each platform and service have various requirements to be handled by domain experts. Still, if the next-generation network management is dependent on manual updates, it will become impossible to provide seamless service provisioning in runtime. Since the traffic for a particular type of service varies significantly over time, automatic provisioning of resources and orchestration in runtime need to be integrated. Besides, with the increase in the number of devices, amount, and variety of traffic, the management of resources with optimization becomes a challenging task. To this end, this manuscript provides a solution that automates the management and service provisioning through multiple platforms while assuring various aspects, including automation, resource management and service assurance. The solution consists of an intent-based system that automatically manages different orchestrators, and eliminates manual control by abstracting the complex configuration requirements into simple and generic contracts. The proposed system considers handling the scalability of resources in runtime by using Machine Learning (ML) to automate and optimize service resource utilization.

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Keywords

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