IoTwins Project Distributed Digital Twins for Industrial SMEs: a - - PowerPoint PPT Presentation

IoTwins Project

Distributed Digital Twins for Industrial SMEs: a Big Data Platform

Wo r k s h o p w i t h I C T 1 1 p r o j e c t s - H P C , B i g D a t a , I o T a n d A I f u t u r e i n d u s t r y - d r i v e n c o l l a b o r a t i v e s t r a t e g i c t o p i c s ( p a r t 2 ) @ B D VA , 0 3 / 0 7 / 2 0 2 0 – F o l l o w - u p w o r k s h o p

Paolo Bellavista

D e p t . C o m p u t e r S c i e n c e a n d E n g i n e e r i n g ( D I S I ) , U n i v e r s i t y o f B o l o g n a

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The Project.

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HYBRID and DISTRIBUTED Digital Twins Concept in IoTwins

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Distributed Training and Control in IoTwins

Future Industry-driven Collaborative Strategic Topics.

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Future industry-driven collaborative strategic topics.

Questions: Data: avoidance of moving data, data curation and anonymizing, cross-IT infrastructure management HPC/Cloud infrastructure to edge: data movement, data sharing and orchestration, use of blockchain technology, cybersecurity, importance for industry (digital Twin context) Stimuli to discussion: Distributed digital twins Distributed digital twins Distributed digital twins Distributed digital twins

No data migration for better ownership, latency reduction, better sustainability (not only economic…)

Distributed cloud continuum infrastructure Distributed cloud continuum infrastructure Distributed cloud continuum infrastructure Distributed cloud continuum infrastructure

Distributed orchestration in open and portable solutions HPC for hybrid digital twins (complex simulations, …) HPC for resource-greedy learning phases in distributed, federated, reinforcement, … learning

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Future industry-driven collaborative strategic topics.

Questions: Workflows: approaches for mastering the complexity and orchestration. Would a reference architecture of workflows be helpful? AI/ML, training: need for automated ML, distributed training, explainability of the decision- making process Stimuli to discussion: Standardized workflow architecture and orchestration

• rchestration
• rchestration
• rchestration could be useful, in particular with

innovative distributed challenges in mind

Distributed cloud continuum Distributed cloud continuum Distributed cloud continuum Distributed cloud continuum

In many application scenarios (smart cities, …, but also I4.0 with data ownership requirements), need for distributed training distributed training distributed training distributed training and better explainability

Distributed, federated, reinforcement, … learning in the distributed cloud continuum

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Distributed Cloud Continuum and Small Data Ecosystem Building.

Making the cloud continuum an industrial reality Making the cloud continuum an industrial reality Making the cloud continuum an industrial reality Making the cloud continuum an industrial reality

Interoperability and common APIs Distributed and portable orchestration Generating trust around the idea of an EU-based cloud continuum, in particular in some specific vertical domains

Extracting value also from “small data” Extracting value also from “small data” Extracting value also from “small data” Extracting value also from “small data” (D. Estrin, Cornell) by building and promoting the emergence of communities, ecosystems, … fueled by fueled by fueled by fueled by companies in the manufacturing domain companies in the manufacturing domain companies in the manufacturing domain companies in the manufacturing domain

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Distributed Cloud Continum and Small Data Ecosystem Building.

Future challenges are future opportunities! An EU An EU An EU An EU-

• based cloud continuum, e.g., for the manufacturing industry

based cloud continuum, e.g., for the manufacturing industry based cloud continuum, e.g., for the manufacturing industry based cloud continuum, e.g., for the manufacturing industry

Interoperability and common APIs Distributed and portable orchestration Support for quality requirements, such as latency, reliability, scalability, … Support for quality requirements, such as latency, reliability, scalability, … Support for quality requirements, such as latency, reliability, scalability, … Support for quality requirements, such as latency, reliability, scalability, … Integration with resource slicing, 5G/6G, Time Sensitive Networking, … Integration with resource slicing, 5G/6G, Time Sensitive Networking, … Integration with resource slicing, 5G/6G, Time Sensitive Networking, … Integration with resource slicing, 5G/6G, Time Sensitive Networking, … Generating trust around the idea of an EU-based cloud continuum, in particular in some specific vertical domains

Extracting value also from “small data” Extracting value also from “small data” Extracting value also from “small data” Extracting value also from “small data” Specialization national/EU districts and the emergence of communities, ecosystems, … which allow also SMEs to reach “the critical mass” for their specific sub-domain

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From IoTwins project perspective, next 2-4 years?

Questions: How does each field impact your project? Specifically, describe how to incorporate HPC processing in your use cases. What are your projects’ plans to provide solutions to these challenges? Stimuli to discussion: HPC for hybrid and distributed digital twins

Examples: simulations of machine tool spindles and closure manufacturing machines

HPC for training

Examples: wind turbine predictive maintenance, holistic supercomputer facility management

Modular platform infrastructure to reduce SME barriers to access these KETs, scalability also towards simpler and more limited solutions Cloud Cloud Cloud Cloud-

• and distributed cloud

and distributed cloud and distributed cloud and distributed cloud-

• oriented perspective
• riented perspective
• riented perspective
• riented perspective

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From IoTwins project perspective, next 2-4 years?

Prioritize the four fields in terms of complexity and importance for R&I calls in Europe and pls. explain your decision Distributed cloud continuum Distributed cloud continuum Distributed cloud continuum Distributed cloud continuum Distributed machine learning over distributed cloud Distributed machine learning over distributed cloud Distributed machine learning over distributed cloud Distributed machine learning over distributed cloud Sustainable ecosystems for small data communities Sustainable ecosystems for small data communities Sustainable ecosystems for small data communities Sustainable ecosystems for small data communities QoS guarantee or control for the I4.0 domain QoS guarantee or control for the I4.0 domain QoS guarantee or control for the I4.0 domain QoS guarantee or control for the I4.0 domain What could be specific contributions of your project partners or other institutions in Europe in each of these areas? See the previous slides…

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Key topics, state of the art, and current limitations (1).

Big Data analytics and AI techniques Big Data analytics and AI techniques Big Data analytics and AI techniques Big Data analytics and AI techniques have an unprecedented chance to bring EU manufacturing companies (product companies, but not only…) into the world of services and digital business The Big Data impact and evolution could be extraordinarily amplified in manufacturing if coupled with proper cloud continuum solutions proper cloud continuum solutions proper cloud continuum solutions proper cloud continuum solutions

to reduce latency latency latency latency to support prompt/reliable distributed control prompt/reliable distributed control prompt/reliable distributed control prompt/reliable distributed control to improve scalability scalability scalability scalability to improve sustainability sustainability sustainability sustainability to enable better privacy and raw data ownership privacy and raw data ownership privacy and raw data ownership privacy and raw data ownership …

Need for more distributed and more explainable AI techniques more distributed and more explainable AI techniques more distributed and more explainable AI techniques more distributed and more explainable AI techniques, first of all for distributed learning and distributed classification/anomaly detection/control

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Key topics, state of the art, and current limitations (2).

Short-term barriers that need to be reduced, in particular for SMEs:

Complex and rapidly evolving tools and techniques to be mastered delays and costs in product/process design, deployment, test, and refinement Deep learning require access to very large sources of curated data very large sources of curated data very large sources of curated data very large sources of curated data, as well as significant significant significant significant computational resources for training computational resources for training computational resources for training computational resources for training Need to be at the premises of the systems Need to be at the premises of the systems Need to be at the premises of the systems Need to be at the premises of the systems generating the big data, e.g., to locally monitor, control, and adapt the components of a manufacturing production line under tight latency and reliability requirements, while preserving an adequate degree of data privacy Need of investments Need of investments Need of investments Need of investments in infrastructure at the server side (where relevant cloud/HPC resources are

• ften needed for model learning and simulation), at the edge side (e.g., to extend manufacturing

machinery and their gateways on the industry plant premises with edge computing functionality), at the communication infrastructure side (5G/6G, Time Sensitive Networking, …), and also in terms of integration efforts

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More general and ambitious concrete actions.

What the EU, the BDVA, our scientific community can do to stimulate the emergence and stimulate the emergence and stimulate the emergence and stimulate the emergence and consolidation of “small data” ecosystems consolidation of “small data” ecosystems consolidation of “small data” ecosystems consolidation of “small data” ecosystems? Are we ready for an EU EU EU EU-

• centric cloud continuum

centric cloud continuum centric cloud continuum centric cloud continuum? Which could be the role of national competence centers and EU Digital Innovation Hubs in that? … More technically oriented: Support research on federated learning and control applied to I4.0 federated learning and control applied to I4.0 federated learning and control applied to I4.0 federated learning and control applied to I4.0 Support research on cloud continuum oriented to SLA on quality cloud continuum oriented to SLA on quality cloud continuum oriented to SLA on quality cloud continuum oriented to SLA on quality requirements, integration with Time Sensitive Networking Time Sensitive Networking Time Sensitive Networking Time Sensitive Networking, virtualization/isolation with guaranteed execution properties guaranteed execution properties guaranteed execution properties guaranteed execution properties, … Definitely an open list for discussion…

www.iotwins.eu @IoTwins_EU info@iotwins.eu

Contacts.

Paolo Bellavista

Professor of Distributed and Mobile Systems DISI – University of Bologna BI-REX I4.0 Competence

paolo.bellavista@unibo.it http://unibo.it/sitoweb/paolo.bellavista/en

Thank you.