College Dean of International
Professor of Performance Engineering,
CARDIFE UNIVERSITY, UK
Omar Rana is a Professor of Performance Engineering and the Dean of International for the Physical Sciences and Engineering College at Cardiff University. He has research interests in high performance distributed computing (particularly cloud and edge computing) and intelligent systems. He is a visitor professor at the Department of Computer Science and Engineering at Shanghai Jiao Tong University (China) and was previously a visiting professor at Princess Noura University in Riyadh (Saudi Arabia). He is a fellow of Cardiff University’s multi-disciplinary “Data Innovation“ Research Institute and previously the deputy director of the Welsh eScience Centre.
Rana has contributed to specification and standardisation activities via the Open Grid Forum and worked as a software developer with London-based Marshall Bio-Technology Limited prior to joining Cardiff University, where he developed specialist software to support biotech instrumentation. He also contributed to public understanding of science, via the Wellcome Trust funded “Science Line“, in collaboration with BBC and Channel 4. Rana holds a PhD in “Neural Computing and Parallel Architectures“ from Imperial College (London University, UK), an MSc in Microelectronics (University of Southampton, UK) and a BEng in Information Systems Eng. from Imperial College (London University, UK).
Internet of Things (IoT) applications today involve data capture from sensors and devices that are close to the phenomenon being measured, with such data subsequently being transmitted to Cloud data centre for storage, analysis and visualisation. Currently devices used for data capture often differ from those that are used to subsequently carry out analysis on such data. Increasing availability of storage and processing devices closer to the data capture device, perhaps over a one-hop network connection or even directly connected to the IoT device itself, requires more efficient allocation of processing across such edge devices and data centres. Supporting machine learning directly on edge devices also enables support for distributed (federated) learning, enabling user devices to be used directly in the inference or learning process. Scalability in this context needs to consider both cloud resources, data distribution and initial processing on edge resources closer to the user. This talk considers whether a data comms. network can be enhanced using edge resources, and whether a combined use of edge, in-network (in-transit) and cloud data centre resources provide an efficient infrastructure for machine learning and AI. The following questions are addressed in this talk:
- How do we partition machine learning algorithms across Edge-Network-Cloud resources — based on constraints such as privacy capacity and resilience?
- Can machine learning algorithms be adapted based on the characteristics of devices on which they are hosted? What does this mean for stability/ convergence vs. performance?
Head of the Research Division of Distributed Systems at the TU Wien, Austria
Schahram Dustdar is Full Professor of Computer Science heading the
Research Division of Distributed Systems at the TU Wien, Austria. He has an H-index of 78 with some 36,000 citations. He holds several honorary positions: University of California (USC) Los Angeles; Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, University Pompeu Fabra, Barcelona, Spain. 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 (CapGemini Ernst & Young) (2002); MERCUR Innovation award of the Austrian Chamber of Commerce (2002).
He is founding co-Editor-in-Chief of 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 Computing Surveys, 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 multiple awards: IEEE TCSVC Outstanding Leadership Award (2018), IEEE TCSC Award for Excellence in Scalable
Computing (2019), ACM Distinguished Scientist (2009), ACM Distinguished Speaker (2021), IBM Faculty Award (2012). He is 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) and an Asia-Pacific Artificial Intelligence Association (AAIA) Fellow (2021).
As humans, things, software and AI continue to become the entangled fabric of distributed systems, systems engineers and researchers are facing novel challenges. In this talk, we analyze the role of IoT, Edge, Cloud, and Human-based Computing as well as AI in the co-evolution of distributed systems for the new decade. We identify challenges and discuss a roadmap that these new distributed systems have to address. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalization to enable seamless end-to-end distributed systems.