Dissecting the impact of information and communication technologies on digital twins as a service

Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.This work was supported in part by the H2020 European Union/Taiwan (EU/TW) Joint Action 5G-eDge Intelligence for Vertical Experimentation (DIVE) under Grant 859881, in part by the H2020 5Growth Project under Grant 856709, in part by the Madrid Government (Comunidad de Madrid-Spain) through the Multiannual Agreement with Universidad Carlos III de Madrid (UC3M) in the line of Excellence of University Professors under Grant EPUC3M21, and in part by the context of the V PRICIT (Regional Program of Research and Technological Innovation)

Toward intelligent cyber-physical systems: Digital twin meets artificial intelligence

Industry 4.0 aims to support smarter and autonomous processes while improving agility, cost efficiency, and user experience. To fulfill its promises, properly processing the data of the industrial processes and infrastructures is required. Artificial intelligence (AI) appears as a strong candidate to handle all generated data, and to help in the automation and smartification process. This article overviews the digital twin as a true embodiment of a cyber-physical system (CPS) in Industry 4.0, showing the mission of AI in this concept. It presents the key enabling technologies of the digital twin such as edge, fog, and 5G, where the physical processes are integrated with the computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure levels. Finally, movement prediction is selected and experimentally validated using real data generated by a digital twin for robotic arms with results showcasing its potential.This work has been (partially) funded by the H2020 EU/TW joint action 5G-DIVE (Grant #859881) and the H2020 5Growth project (Grant #856709). It has also been funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52/AEI/10.13039/501100011033)

An Intelligent Edge-based Digital Twin for Robotics

This paper has been presented at 2020 IEEE Globecom Workshop on Advanced Technology for 5G Plus.Digital Twin is one of the use cases targeted by the fourth industrial revolution (Industry 4.0), which, through the digitalization of the robotic systems, will enable enhanced automation and remote controlling capabilities. Building upon this concept, this work proposes a solution for an Edge-based Digital Twin for robotics, which leverages on the cloud-to-things continuum to offload computation and intelligence from the robots to the network. This imposes stringent requirements over the communication technologies which are fulfilled by relying on 5G. This solution is implemented in an E2E scenario combining the cloud-to-things continuum, 5G connectivity and intelligence capabilities and validated through a set of experimental evaluations. Results show not only that offloading the robot's functions to the edge is feasible when supported by the 5G connectivity, but also the benefits of introducing intelligence and automation.This work has been (partially) funded by H2020 EU/TW 5G-DIVE (Grant 859881) and H2020 5Growth (Grant 856709). It has been also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019-108713RB-C52 / AEI / 10.13039/501100011033)

OpenFlowMon: a fully distributed monitoring framework for virtualized environments

Proceedings of: 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 9 November 2021, Heraklion, Greece.Network monitoring allows a continuous assessment on the health and performance of the network infrastructure. With the significant change on how networks are deployed and operated, mainly due to the advent of virtualization technologies, alternative monitoring approaches are emerging to provide a finer-grained flow monitoring to complement already existing mechanisms and capabilities. In this paper, we proposed and developed an Open-Source Flow Monitoring Framework (OpenFlowMon), a fully distributed monitoring framework implemented solely with open-source solutions. This framework is used to assess the performance and the overhead introduced by two different flow monitoring approaches: (i) switch level and (ii) compute node level monitoring. Results show that monitoring at compute node level not only reduces the overhead but also mitigates a potential complex post-processing in east-to-west traffic.This work has been (partially) funded by H2020 EU/TW 5G-DIVE (Grant 859881) and H2020 5Growth (Grant 856709)

FoReCo: a forecast-based recovery mechanism for real-time remote control of robotic manipulators

Wireless communications represent a game changer for future manufacturing plants, enabling flexible production chains as machinery and other components are not restricted to a location by the rigid wired connections on the factory floor. However, the presence of electromagnetic interference in the wireless spectrum may result in packet loss and delay, making it a challenging environment to meet the extreme reliability requirements of industrial applications. In such conditions, achieving real-time remote control, either from the Edge or Cloud, becomes complex. In this paper, we investigate a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses Machine Learning (ML) to infer lost commands caused by interference in the wireless channel. FoReCo is evaluated through both simulation and experimentation in interference prone IEEE 802.11 wireless links, and using a commercial research robot that performs pick-and-place tasks. Results show that in case of interference, FoReCo trajectory error is decreased by x18 and x2 times in simulation and experimentation, and that FoReCo is sufficiently lightweight to be deployed in the hardware of already used in existing solutions.This work has been partially funded by European Union's Horizon 2020 research and innovation programme under grant agreement No 101015956, and the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D 6GEDGEDT and 6G-DATADRIVE

Dimensioning V2N services in 5G networks through forecast-based scaling

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as remote driving, cooperative awareness, and hazard warning) will have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.Work partially funded by the EU H2020 5GROWTH Project (grant no. 856709) and H2020 collaborative Europe/Taiwan research project 5G-DIVE (grant no. 859881)

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)

Public and non-public network integration for 5Growth Industry 4.0 use cases

5G is playing a paramount role in the digital transformation of the industrial sector, offering high-bandwidth, reliable, and low-latency wireless connectivity to meet the stringent and critical performance requirements of manufacturing processes. This work analyzes the applicability of 5G technologies as key enablers to support, enhance, and even enable novel advances in Industry 4.0. It proposes a complete 5G solution for two real-world Industry 4.0 use cases related to metrology and quality control. This solution uses 5Growth to ease and automate the management of vertical services over a soft-ware-defined network and network function virtualization based 5G mobile transport and computing infrastructure, and to aid the integration of the verticals' private 5G network with the public network. Finally, a validation campaign assesses the applicability of the proposed solution to support the performance requirements (especially latency and user data rate) of the selected use cases, and evaluates its efficiency regarding vertical service setup time across different domains in less than three minutes.This work has been partially supported by the EC H2020 5GPPP 5Growth project (Grant 856709) and the H2020 5G-EVE project (Grant 815074)

Multi-domain solutions for the deployment of private 5G networks

Private 5G networks have become a popular choice of various vertical industries to build dedicated and secure wireless networks in industry environments to deploy their services with enhanced service flexibility and device connectivity to foster industry digitalization. This article proposes multiple multi-domain solutions to deploy private 5G networks for vertical industries across their local premises and interconnecting them with the public networks. Such scenarios open up a new market segment for various stakeholders, and break the current operators' business and service provisioning models. This, in turn, demands new interactions among the different stakeholders across their administrative domains. To this aim, three distinct levels of multi-domain solutions for deploying vertical's 5G private networks are proposed in this work, which can support interactions at different layers among various stakeholders, allowing for distinct levels of service exposure and control. Building on a set of industry verticals (comprising Industry 4.0, Transportation and Energy), different deployment models are analyzed and the proposed multi-domain solutions are applied. These solutions are implemented and validated through two proof-of-concept prototypes integrating a 5G private network platform (5Growth platform) with public ones. These solutions are being implemented in three vertical pilots conducted with real industry verticals. The obtained results demonstrated the feasibility of the proposed multi-domain solutions applied at the three layers of the system enabling various levels of interactions among the different stakeholders. The achieved end-to-end service instantiation time across multiple domains is in the range of minutes, where the delay impact caused by the resultant multi-domain interactions is considerably low. The proposed multi-domain approaches offer generic solutions and standard interfaces to support the different private network deployment models.This work was supported in part by the European Commission (EC) H2020 5GPPP 5Growth Project under Grant 856709, and in part by the H2020 5G European Validation platform for Extensive trials (5G EVE) Project under Grant 815074

5Growth: An end-to-end service platform for automated deployment and management of vertical services over 5G networks

This article introduces the key innovations of the 5Growth service platform to empower vertical industries with an AI-driven automated 5G end-to-end slicing solution that allows industries to achieve their service requirements. Specifically, we present multiple vertical pilots (Industry 4.0, transportation, and energy), identify the key 5G requirements to enable them, and analyze existing technical and functional gaps as compared to current solutions. Based on the identified gaps, we propose a set of innovations to address them with: (i) support of 3GPP-based RAN slices by introducing a RAN slicing model and providing automated RAN orchestration and control; (ii) an AI-driven closed-loop for automated service management with service level agreement assurance; and (iii) multi-domain solutions to expand service offerings by aggregating services and resources from different provider domains and also enable the integration of private 5G networks with public networks.This work has been partially supported by EC H2020 5GPPP 5Growth project (Grant 856709)