AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions

The foreseen complexity in operating and managing 5G and beyond networks has propelled the trend toward closed-loop automation of network and service management operations. To this end, the ETSI Zero-touch network and Service Management (ZSM) framework is envisaged as a next-generation management system that aims to have all operational processes and tasks executed automatically, ideally with 100 percent automation. Artificial Intelligence (AI) is envisioned as a key enabler of self-managing capabilities, resulting in lower operational costs, accelerated time-tovalue and reduced risk of human error. Nevertheless, the growing enthusiasm for leveraging AI in a ZSM system should not overlook the potential limitations and risks of using AI techniques. The current paper aims to introduce the ZSM concept and point out the AI-based limitations and risks that need to be addressed in order to make ZSM a reality.

[1]  Qinghai Yang,et al.  Machine Learning Aided Context-Aware Self-Healing Management for Ultra Dense Networks With QoS Provisions , 2018, IEEE Transactions on Vehicular Technology.

[2]  Christopher Leckie,et al.  Reinforcement Learning for Autonomous Defence in Software-Defined Networking , 2018, GameSec.

[3]  Nei Kato,et al.  State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems , 2017, IEEE Communications Surveys & Tutorials.

[4]  Tarik Taleb,et al.  ZSM Security: Threat Surface and Best Practices , 2020, IEEE Network.

[5]  Marco Gramaglia,et al.  Mobile traffic forecasting for maximizing 5G network slicing resource utilization , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[6]  Li Wang,et al.  Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges , 2018, IEEE Communications Magazine.

[7]  Janne Ali-Tolppa,et al.  SELF-HEALING AND RESILIENCE IN FUTURE 5G COGNITIVE AUTONOMOUS NETWORKS , 2018, 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K).

[8]  Adlen Ksentini,et al.  Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach , 2018, IEEE Network.

[9]  Fulvio Risso,et al.  An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC , 2017, 2017 13th International Conference on Network and Service Management (CNSM).

[10]  Igor G. Olaizola,et al.  Network Resource Allocation System for QoE-Aware Delivery of Media Services in 5G Networks , 2018, IEEE Transactions on Broadcasting.

[11]  Ali A. Ghorbani,et al.  Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization , 2018, ICISSP.

[12]  Junaid Qadir,et al.  Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward , 2018, 2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops).

[13]  Blaine Nelson,et al.  Can machine learning be secure? , 2006, ASIACCS '06.