AI-driven Closed-loop Automation in 5G and beyond Mobile Networks

被引：12

作者：

Boutaba, Raouf ^{[1
]}

Shahriar, Nashid ^{[2
]}

Salahuddin, Mohammad A. ^{[1
]}

Chowdhury, Shihabur R. ^{[1
]}

Saha, Niloy ^{[1
]}

James, Alexander ^{[1
]}

机构：

[1] Univ Waterloo, Waterloo, ON, Canada

[2] Univ Regina, Regina, SK, Canada

来源：

PROCEEDINGS OF THE 4TH FLEXNETS WORKSHOP ON FLEXIBLE NETWORKS, ARTIFICIAL INTELLIGENCE SUPPORTED NETWORK FLEXIBILITY AND AGILITY (FLEXNETS'21) | 2021年

关键词：

5G; artificial intelligence; machine learning; closed-loop orchestration and management;

D O I：

10.1145/3472735.3474458

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The 5th Generation (5G) mobile networks support a wide range of services that impose diverse and stringent QoS requirements. This will be further exacerbated with the evolution towards 6th Generation mobile networks. Inevitably, 5G and beyond mobile networks must provide stricter, differentiated QoS guarantees to meet the increasing demands of future applications, which cannot be satisfied with traditional human-in-the-loop service orchestration and network management approaches. In this paper, we lay out our vision for closed-loop service orchestration and network management of 5G and beyond mobile networks. We extend the MAPE (i.e., monitor, analyze, plan, and execute) control loop to facilitate closed-loop automation, and discuss the quintes-sential role of Artificial Intelligence/Machine Learning in its realization. We also instigate open research challenges for closed-loop automation of 5G and beyond mobile networks.

引用

页码：1 / 6

页数：6

共 25 条

[1] Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions [J].

Afolabi, Ibrahim ;

Taleb, Tarik ;

Samdanis, Konstantinos ;

Ksentini, Adlen ;

Flinck, Hannu .

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (03) :2429-2453

[2]

[Anonymous], 2020, 28554 3GPP

[3]

[Anonymous], 2020, 0091 ETSI GS ZSM

[4]

[Anonymous], 2020, 28552 3GPP

[5] Machine Learning for Cognitive Network Management [J].

Ayoubi, Sara ;

Limam, Noura ;

Salahuddin, Mohammad A. ;

Shahriar, Nashid ;

Boutaba, Raouf ;

Estrada-Solano, Felipe ;

Caicedo, Oscar M. .

IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (01) :158-165

[6] 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges [J].

Barakabitze, Alcardo Alex ;

Ahmad, Arslan ;

Mijumbi, Rashid ;

Hines, Andrew .

COMPUTER NETWORKS, 2020, 167

[7] PINT: Probabilistic In-band Network Telemetry [J].

Ben Basat, Ran ;

Ramanathan, Sivaramakrishnan ;

Li, Yuliang ;

Antichi, Gianni ;

Yu, Minlan ;

Mitzenmacher, Michael .

SIGCOMM '20: PROCEEDINGS OF THE 2020 ANNUAL CONFERENCE OF THE ACM SPECIAL INTEREST GROUP ON DATA COMMUNICATION ON THE APPLICATIONS, TECHNOLOGIES, ARCHITECTURES, AND PROTOCOLS FOR COMPUTER COMMUNICATION, 2020, :662-680

[8] A comprehensive survey on machine learning for networking: evolution, applications and research opportunities [J].