Demo: AIML-as-a-service for SLA management of a digital twin virtual network service

Proceedings of: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).This demonstration presents an AI/ML platform that is offered as a service (AIMLaaS) and integrated in the management and orchestration (MANO) workflow defined in the project 5Growth following the recommendations of various standardization organizations. In such a system, SLA management decisions (scaling, in this demo) are taken at runtime by AI/ML models that are requested and downloaded by the MANO stack from the AI/ML platform at instantiation time, according to the service definition. Relevant metrics to be injected into the model are also automatically configured so that they are collected, ingested, and consumed along the deployed data engineering pipeline. The use case to which it is applied is a digital twin service, whose control and motion planning function has stringent latency constraints (directly linked to its CPU consumption), eventually determining the need for scaling out/in to fulfill the SLA.Work supported in part by EU Commission H2020 5Growth project (Grant No. 856709) and H2020 Europe/Taiwan 5G-Dive project (Grant No. 859881)

Slice Isolation for 5G Transport Networks

Network slicing plays a key role in the 5G ecosystem for vertical industries to introduce new services. However, one widely-recognized challenge of network slicing is to provide traffic isolation and concurrently satisfy diverse performance requirements, e.g., bandwidth and latency. In this work, we showcase the capability to retain these two goals at the same time, via extending the 5Growth baseline architecture and designing a new data-plane pipeline, i.e., virtual queue, over the P4 switch. To demonstrate the effectiveness of our approach, a proof-of-concept is presented serving different service requests over a mixed data path, including P4 switches and Open vSwitches (OvSs)

On the Integration of AI/ML-based scaling operations in the 5Growth platform

This paper has been presented at 2020 IEEE Conference on Network Function Virtualization and Software Defined NetworksThe automated assurance of vertical service level agreements (SLA) is a challenge in 5G networks. The EU 5Growth project designs and develops a 5G End-to-End service platform that integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques for any decision-making process in the management and orchestration (MANO) stack. This paper presents the detailed architecture and first prototype of the 5Growth platform taking AI/ML-based network service auto-scaling decisions. This also includes the modification of the ETSI network service descriptors for requesting AI/ML-based decisions for orchestration problems and the integration of a data engineering pipeline for real-time data gathering and model execution. Our evaluation shows that AI/ML-related service handling operations (1&-2 s.) are well below instantiation/termination procedures (80/60 s., respectively). Furthermore, online classification can be performed in the order of hundreds of milliseconds (600 ms).This work has been partially funded by the EU H2020 5Growth Project (grant no. 856709), by MINECO grant TEC2017-88373-R (5G-REFINE) and Generalitat de Catalunya grant 2017 SGR 1195

NFV Service Federation: enabling Multi-Provider eHealth Emergency Services

One of the key challenges in developing 5G/6G is to offer improved vertical service support providing enlarged service flexibility, coverage and connectivity while enhancing the business relations among different stakeholders. To address this challenge, Network Service Federation (NSF) is a required feature to enable the deployment and the management of vertical services that may span multiple provider domains owned by different operators and/or service providers. In this demonstration, we show our proposed NSF solution to dynamically deploy an eHealth network service across multiple provider domains at different locations.Work supported in part by EU Commission H2020 5G-TRANSFORMER project (Grant No. 761536), H2020 5Growth project (Grant No. 856709), Spanish MINECO grant TEC2017-88373-R (5G-REFINE) and Generalitat de Catalunya grant 2017 SGR 1195

Performance Isolation for Network Slices in Industry 4.0: The 5Growth Approach

Network slicing plays a key role in the 5G ecosystem for verticals to introduce new use cases in the industrial sector, i.e., Industry 4.0. However, a widely recognized challenge of network slicing is to provide traffic isolation and concurrently satisfy diverse performance requirements, e.g., bandwidth and latency. Such challenge becomes even more important when serving a large number of network traffic flows under a resource-limited condition between distributed sites, e.g., factory floor and remote office. In this work, we present the capability to retain these two goals at the same time, by applying the virtual queue notion over a priority queuing based pipeline in P4 switch over software-defined networks. To examine the effectiveness of our approach, a proof-of-concept is setup to serve different requests of Industry 4.0 use cases over a mixed data path, including P4 switch and Open vSwitch, for a large number of network flows

5Growth: Secure and reliable network slicing for verticals

Proceedings of: Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 8-11 June 2021, Porto, Portugal.Network slicing is well-recognized as a core 5G technology to enable heterogeneous vertical services sharing the same infrastructure. In this context, the H2020 5Growth project extends baseline 5G management and orchestration platforms to manage the life-cycle of real end-to-end, reliable, and secure network slices with performance guarantees. In this paper, we present 5Growth's approaches to (i) attain isolation across network slices, (ii) provide secure interfaces towards third parties, and (iii) exploit AI/ML to achieve reliability through automated anomaly detection. In our quest towards validating full-fledged 5G pilots, we demonstrate our slicing mechanisms in PoCs that include interacting with ICT-17 infrastructure.This work has been partially supported by EC H2020 5GPPP 5Growth project (Grant 856709)