Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications

Abstract

I. INTRODUCTION R ESEARCH in large-scale networking systems has been shaped and will continue to be guided by specific characteristics of applications and the underlying platforms and infrastructures. On the one hand, applications are growing at an accelerated pace, which is fundamentally unpredictable in both breadth and depth. On the other hand, the underlying networking has been the focus of a huge transformation enabled by new models resulting from virtualization and cloud computing. This has led to a number of novel architectures supported by emerging technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and more recently, edge cloud and fog networking, or network slicing [1], [2]. This evolution towards enhanced design opportunities along with increasing complexity in networking and its applications has fueled the need for improved network automation in agile infrastructures. At the same time, their complexity has dramatically increased. The networking dynamics have had the effect of making it even more important and challenging to design scalable network measurement and analysis techniques and associated tools. Critical applications such as resource allocation, network monitoring, security enforcement, or dynamic network management require real-time mechanisms for online analysis as well as efficient techniques for offline deep analysis of massive historical data.

Research and Analysis of Sports Training Real-Time Monitoring System Based on Mobile Artificial Intelligence Terminal

“…The database of this architecture is composed of two parts: client application program and database server program, which are called foreground program and background program, respectively. The whole system consists of electromyogram collector, interface card, synchronizer and electromyogram acquisition, and analysis software [18]. For collective events, the tactical cooperation and tactical choice between athletes are important factors that determine the competition.…”

Learning based E2E Energy Efficient in Joint Radio and NFV Resource Allocation for 5G and Beyond Networks

“…Also, due to the fact that different parts of the network interact with each other, it is necessary to consider all parts of the network together to optimize the total energy consumption. Machine learning (ML) are new approaches to solve optimization problems in all domains, e.g., network domain, that have recently received a great deal of attention [14]- [16]. ML, due to its data-driven nature, automatically learns the network and communication environment and dynamically adapts protocols without human intervention [14]- [16].…”

Cloud Network Slicing: A systematic mapping study from scientific publications

“…Service Assurance (0,1%) represent less than a half of the papers included in the Orchestration facet. In [40], AI (Artificial Intelligence) and ML (Machine Learning) are studied in-depth, and the authors concluded that it is necessary to have special care in using these approaches due to the great complexity of data in computer networks.…”

“…Artificial Intelligence algorithms can be used to aid decision making that is part of VNF placement and elasticity of resources [40]. Some of the candidate machine learning algorithms to be used are the ones related to predictions, such as linear regression.…”