Keynote Speakers

Herodotos Herodotou

Cyprus University of Technology, Cyprus

Title: Hyper-Distributed Intelligent Computing in the Cloud-Edge-IoT Continuum

Abstract: Modern data-driven applications increasingly need to run across cloud, edge, and IoT environments to meet demands for low-latency processing, real-time decision-making, scalability, and localized data handling. However, they face key challenges such as the complexity of managing and orchestrating distributed workloads efficiently, limited computational resources at the edge, data consistency across heterogeneous environments, and security and privacy concerns. The HYPER-AI platform addresses the aforementioned challenges across the compute continuum, enabling hyper-distributed execution of highly demanding data processing applications. To give applications access to computational, storage, or network services, HYPER-AI implements the idea of computing swarms as autonomous, self-organized, and opportunistic networks of smart nodes. These networks may offer a diverse and heterogeneous set of resources (processing, storage, data, communication) at all levels and can dynamically connect, interact, and cooperate. The objective is to make smart multi-node (swarm) deployment scenario design, execution, and monitoring easier, through appropriate intelligent agents for self-configuration (nodes assigned resources), self-healing (swarmed nodes lifecycle), self-optimizing (exploiting built-in situation awareness mechanisms), and self-protecting (intrusion detection, privacy, security, and encryption) at application runtime. HYPER-AI also proposes semantic representation concepts to enable heterogeneous resources’ abstraction in a homogeneous way, under a common annotation, across the whole range of the compute continuum. This presentation discusses the design goals, core concepts, and technical approaches of HYPER-AI toward enabling smarter, faster, and greener applications in our increasingly data-driven world.

Bio: Dr. Herodotos Herodotou is an Associate Professor in the Department of Electrical Engineering and Computer Engineering and Informatics at the Cyprus University of Technology, leading the Data Intensive Computing Research Lab. He received his Ph.D. in Computer Science from Duke University in May 2012. His Ph.D. dissertation received the ACM SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention as well as the Outstanding Ph.D. Dissertation Award in Computer Science at Duke. Before joining CUT, he held research positions at Microsoft Research, Yahoo! Labs, and Aster Data, as well as software engineering positions at Microsoft and RWD Technologies. His research interests include large-scale Data Processing Systems, Database Systems, and Cloud Computing. In particular, his work focuses on ease of use, manageability, and automated tuning of both centralized and distributed data-intensive computing systems. In addition, he is interested in applying database and dataflow system techniques in other domains like maritime informatics, smart tourism, social computing, and environmental informatics. His research work has been published in several top scientific conferences and journals (e.g., PVLDB, SIGMOD, SoCC, CIDR), four books, and two book chapters.