5G-MoNArch use case for ETSI ENI: elastic resource management and orchestration

Proceeding of: 2018 IEEE Conference on Standards for Communications and Networking (CSCN)5G networks will grant spectacular improvements of the most relevant Key Performance Indicators (KPIs) while allowing resource multi-tenancy through network slicing. However, the other side of the coin is represented by the huge increase of the management complexity and the need for efficient algorithms for resource orchestration. Therefore, the management and orchestration of the network through Artificial Intelligence (AI) and Machine Learning (ML) algorithms is considered a promising solution, as it allows to reduce the human interaction (usually expensive and error-prone) and scale to large scenario composed by thousands of slices in heterogeneous environments. In this paper, we provide a review of the current standardization efforts in this field, mostly due to the work performed by the Experiential Network Intelligence (ENI) industry specification group (ISG) within the European Telecommunications Standards Institute (ETSI). Then, we thoroughly describe an exemplary use case on elastic network management and orchestration through learning solutions proposed by the 5GPPP project 5G-MoNArch and recently approved at ETSI ENI

Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. A softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this article, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.Part of this work has been performed within the 5G-MoNArch project (Grant Agreement No. 761445), part of the Phase II of the 5th Generation Public Private Partnership (5G-PPP) program partially funded by the European Commission within the Horizon 2020 Framework Program. This work was also supported by the the 5G-Transformer project (Grant Agreement No. 761536)