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Keynote Lectures

Edge Intelligence - The Co-evolution of Humans, IoT, and AI
Schahram Dustdar, Vienna University of Technology, Austria

New Horizons in IoT Workflows Provisioning in Edge and Cloud Datacentres for Fast Data Analytics: The Osmotic Computing Approach
Rajiv Ranjan, Newcastle University, United Kingdom

Reference Architectures for Cloud-based Platforms: Convergence vs. Diversification
Robert Lovas, SZTAKI, Hungary


 

Edge Intelligence - The Co-evolution of Humans, IoT, and AI

Schahram Dustdar
Vienna University of Technology
Austria
 

Brief Bio
Schahram Dustdar is Full Professor of Computer Science heading the Research Division of Distributed Systems at the TU Wien, Austria. He holds several honorary positions: University of California (USC) Los Angeles; Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, and University of Groningen (RuG), The Netherlands (2004-2010). From Dec 2016 until Jan 2017 he was a Visiting Professor at the University of Sevilla, Spain and from January until June 2017 he was a Visiting Professor at UC Berkeley, USA.

From 1999 - 2007 he worked as the co-founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by Engineering NetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. Caramba Labs was nominated for several (international and national) awards: World Technology Award in the category of Software (2001); Top-Startup companies in Austria (Cap Gemini Ernst & Young) (2002); MERCUR Innovation award of the Austrian Chamber of Commerece (2002).

He is founding co-Editor-in-Chief of the new ACM Transactions on Internet of Things (ACM TIoT) as well as Editor-in-Chief of Computing (Springer). He is an Associate Editor of IEEE Transactions on Services Computing, IEEE Transactions on Cloud Computing, ACM Transactions on the Web, and ACM Transactions on Internet Technology, as well as on the editorial board of IEEE Internet Computing and IEEE Computer. Dustdar is recipient of the ACM Distinguished Scientist award (2009), the IBM Faculty Award (2012), an elected member of the Academia Europaea: The Academy of Europe, where he is chairman of the Informatics Section, as well as an IEEE Fellow (2016).


Abstract
Edge AI and Human Augmentation are two major technology trends, driven by recent advancements in edge computing, IoT, and AI accelerators. As humans, things and AI continue to grow closer together, systems engineers and researchers are faced with new and unique challenges. In this talk, we analyse the role of Edge computing and AI in the cyber-human evolution and identify challenges that Edge computing systems will consequently be faced with. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalisation to enable seamless end-to-end Edge intelligence systems.



 

 

New Horizons in IoT Workflows Provisioning in Edge and Cloud Datacentres for Fast Data Analytics: The Osmotic Computing Approach

Rajiv Ranjan
Newcastle University
United Kingdom
 

Brief Bio
Professor Rajiv Ranjan is an Australian-British computer scientist, of Indian origin, known for his research in Distributed Systems (Cloud Computing, Big Data, and the Internet of Things). He is University Chair Professor for the Internet of Things research in the School of Computing of Newcastle University, United Kingdom. He is an internationally established scientist in the area of Distributed Systems (having published about 300 scientific papers). He has secured more than $12 Million AUD (£6 Million+ GBP) in the form of competitive research grants from both public and private agencies. He is an innovator with strong and sustained academic and industrial impact and a globally recognized R&D leader with the proven track record. He serves on the editorial boards of top quality international journals including IEEE Transactions on Computers (2014-2016), IEEE Transactions on Cloud Computing, ACM Transactions on the Internet of Things, The Computer (Oxford University), The Computing (Springer) and Future Generation Computer Systems. He led the Blue Skies section (department, 2014-2019) of IEEE Cloud Computing, where his principal role was to identify and write about most important, cutting-edge research issues at the intersection of multiple, inter-dependent research disciplines within distributed systems research area including Internet of Things, Big Data Analytics, Cloud Computing, and Edge Computing. He is one of the highly cited authors in computer science and software engineering worldwide (h-index=49, g-index=130, and 14000+ google scholar citations; h-index=36 and 7600+ scopus citations; and h-index=30 and 4900+ Web of Science citations).


Abstract
Supporting Internet of Things (IoT) workflow enactment/execution on a combination of computational resources at the network edge and at a datacentre remains a challenge. Increasing volumes of data being generated through smart phones, IoT (Internet of Things) devices (which can vary significantly in scope and capability), need to be processed in a timely manner. Current practice involves using edge nodes (e.g. sensors or other low-capacity devices) as a means to acquire/collect data (i.e. as an "observation" mechanism). Subsequently, this data is transmitted to a datacentre/cloud for analysis/insight. Increasingly, the limitation with the use of a large-scale, centralised datacentre is being realised (such as speed of response for latency-sensitive applications), with the emergence of a number of paradigms to address this concern -- such as fog computing, edge computing, Cloud-of-Things, etc. All of these propose the use of dedicated servers (with varying capacity and capability) within micro/nano datacentres at the network edge, to overcome latency constraints associated with moving data to a central facility, and (lack of use of) increasing computational capability within edge devices. These paradigms also closely align with work in content distribution networks (e.g. from Akamai CDNs), which attempt to place data servers within one (or a small number of) hop of end users (currently 85% of users are supported in this way, with >175K Akamai servers)

A key objective of this keynote talk is to understand how such emerging paradigms can be used to enable cloud systems (supported through large scale computational facilities) to be "stretched" to the network edge, to enable data-driven IoT workflows to be enacted efficiently over such combined infrastructure. We propose the combined use of (varying) capability at the network edge (referred to as an "Edge Datacentre" (EDC)) with capability within a Cloud Datacentre (CDC). Collectively, IoT devices and edge resources, like gateways (Raspberry Pi 3), software-defined network systems (Huawei CloudEngine 6800) and smart phones equipped with sensors, constitute a new set of computing resources -- and as potential components of an EDC. The keynote talk will have the following outline:

1.Overview of the research challenges involved with composing and orchestrating complex IoT workflows in cloud-edge continuum infrastructure

2.Discuss two case studies in healthcare and smart cities domain to understand how data-driven workflows can be applied to create/compose next-generation IoT applications.

3.Discuss our experience with running United Kingdom’s largest IoT infrastructure, namely, the Urban Observatory (http://www.urbanobservatory.ac.uk/)




 

 

Reference Architectures for Cloud-based Platforms: Convergence vs. Diversification

Robert Lovas
SZTAKI
Hungary
 

Brief Bio

Robert Lovas (PhD habil.) is the Vice Director of the Institute for Computer Science and Control (SZTAKI), and Director of the Institute for Cyber-Physical Systems, John von Neumann Faculty of Informatics, Óbuda University.

He received his MSc degree in Electrical Engineering and PhD degree in Informatics from the Budapest University of Technology and Economics, and habilitated at the Óbuda University.

His solid experience in wide range of research and development fields of computational chemistry, numerical meteorological modeling, bioinformatics, precision agriculture, connected Cars, and Industry 4.0 has been gained in various global, European and national ICT collaborations with academic organizations, universities, and enterprises.

He coordinated two e-infrastructure projects in the EU 7th Framework Programme to build computing services for research communities. In the Agrodat.hu project he led the Big Data research platform activities, and he currently acts as a workpackage leader in the H2020 CloudiFacturing and NEANIAS projects addressing innovative cloud and AI services.

More than 80 scientific papers and book chapters on software engineering for Grid, Cloud, Big Data, IoT and AI platforms, particularly from workflow-oriented design, orchestration, debugging, and application aspects, are included in his publication records.


Abstract

We are the witnesses of new emerging trends in computing platforms; software-container based approaches, GPGPU-enabled AI solutions, and more-and-more sophisticated cloud services are available for Big Data, IoT and other categories of widespread applications. Software containers play crucial roles to make complex functionalities available on a diverse set of platforms; there is a clear sign of convergence in this field. Some complex services can be also reused with little effort in multiple sectors as well, e.g. IoT back-ends for Connected Cars and precision agriculture. However, the IT experts still face several problems when they attempt to create, efficiently manage, scale out or orchestrate such set of building blocks in various, diverse IT environments in order to improve their non-functional features (including reduced vendor-locking or higher service reliability). 3rd party solutions from the cloud providers, and state-of-the-art open source tools for on-premise/public/hybrid deployments might be taken into considerations leveraging on the approach of new generation of reference architectures (blueprints) to enable high-level convergence. The invited talk gives an overview of the latest results in this field covering the achievements of some EU projects (H2020 COLA, CloudiFacturing, NEANIAS and EOSC-hub), and national initiatives (e.g. Agrodat.hu and HRDA) that address such challenging topics.




 



 


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