Sebastian Uitz and Michael Steinkellner showcased their game, A Webbing Journey, at the A1 Austria eSports Festival in the Austria Center Vienna on May 27, 2023. The booth, featuring two PCs, a Steam Deck, and a Nintendo Switch, offered players of all ages a delightful experience. Valuable feedback was gathered, fueling the team’s determination to enhance the game for future events.

We are grateful for the positive response and eagerly await incorporating the feedback received. With its endearing storyline and unique gameplay mechanics, the game continues to build anticipation for its official release, offering an enchanting adventure filled with exploration and heartwarming quests.

Call for Papers

Network-assisted video streaming has become a substantial part of modern multimedia applications, enabling users to access high-quality video content over different networks, including the Internet and wireless networks. Efficiently delivering video content over networks poses numerous challenges, such as limited bandwidth, varying network conditions, heterogeneous end devices, and diverse user preferences. Network-assisted video streaming approaches leverage modern networking technologies, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, to not only improve the users’ Quality of Experience (QoE) but also enhance network utilization. Read more

Title: Designing A Sustainable Serverless Graph Processing Tool on the Computing Continuum

Authors: Reza Farahani, Sashko Ristov, and Radu Prodan

29th International European Conference on Parallel and Distributed Computing, , LIMASSOL, CYPRUS, 28 August–1 September 2023

Abstract: Graph processing has become increasingly popular and essential for solving complex problems in various domains, like social networks. However, processing graphs at a massive scale poses critical challenges, such as inefficient resource and energy utilization. To bridge such challenges, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. This paper presents an initial architectural design for the Choreographer, one of the five Graph-Massivizer tools. We explain Choreographer’s components and their collaboration with other Graph-Massivizer tools. We demonstrate how Choreographer can adopt the emerging serverless computing paradigm to process Basic Graph Operations (BGOs) as serverless functions across the computing continuum efficiently. Moreover, we present an early vision of our federated Function-as-a-Service (FaaS) testbed, which will be used to conduct experiments and assess Choreographer performance. The Way Forward: Workshop on Future Challenges in Software Engineering


Josef Hammer presented the poster Unique Prefix vs. Unique Mask for Minimizing SDN Flows with Transparent Edge Access” at the 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023) and the paper “Scalable Transparent Access to 5G Edge Services” at the 7th IEEE International Conference on Fog and Edge Computing (ICFEC 2023), both in Bangalore, India.
AuthorsJosef Hammer and Hermann Hellwagner – Alpen-Adria-Universität Klagenfurt
Abstract: The challenging demands for the next generation of the Internet of Things have led to a massive increase in edge computing and network virtualization technologies. One significant technology is Multi-access Edge Computing (MEC), a central piece of 5G telecommunication systems. MEC provides a cloud computing platform at the edge of the radio access network and is particularly essential to satisfy the challenging low-latency demands of future applications. Our previous publications argue that edge computing should be transparent to clients. We introduced an efficient solution to implement such a transparent approach, leveraging Software-Defined Networking (SDN) and virtual IP+port addresses for registered edge services. Building on our already efficient approach, in this work, we propose significant improvements to scale our transparent solution to large-scale real-world access networks. First, by improving the modularity of our SDN controller design, we enable various options to distribute both the SDN controller’s load and the switches’ flows. Second, we introduce the Unique Mask, a solution superior to the Unique Prefix presented in our previous work that considerably reduces the number of required flows in the switches. Our evaluations show that both algorithms perform very well, with the Unique Mask capable of reducing the number of flows by up to 98 %.
For more information about the research, visit the website:

Authors: Negin Ghamsarian, Javier Gamazo Tejero, Pablo Márquez Neila, Sebastian Wolf, Martin Zinkernagel, Klaus Schoeffmann, and Raphael Sznitman

26th Medical Image Computing and Computer-Assisted Intervention 2023 (MICCAI 2023), Vancouver, Canada, 8-12 October 2023

Abstract: Models capable of leveraging unlabelled data are crucial in overcoming large distribution gaps between the acquired datasets across different imaging devices and configurations. In this regard, self-training techniques based on pseudo-labeling have been shown to be highly effective for unsupervised domain adaptation. However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps. Since the neural network performance should be invariant to image transformations, we look to this fact to identify uncertain pseudo labels. Indeed, we argue that transformation invariant detections can provide more reasonable approximations of ground truth. Accordingly, we propose an unsupervised domain adaptation strategy termed transformation-invariant self-training (TI-ST) to assess pixel-wise pseudo-labels’ reliability and filter out unreliable detections during self-training. We perform comprehensive evaluations for domain adaptation using three different modalities of medical images, two different network architectures, and several alternative state-of-the-art domain adaptation methods. Experimental results confirm the superiority of our proposed method in mitigating the lack of target domain annotation and boosting segmentation performance in the target domain.

  • CP-Steering: CDN- and Protocol-Aware Content Steering Solution for HTTP Adaptive Video Streaming
    Reza Farahani (University of Klagenfurt, Austria), Abdelhak Bentaleb (Concordia University, Canada), Mohammad Shojafar (University of Surrey, UK), Hermann Hellwagner (University of Klagenfurt, Austria)
  • Context-Aware HTTP Adaptive Video Streaming Utilizing QUIC’s Stream Priority
    Sindhu Chellappa (University of New Hampshire), Reza Farahani (University of Klagenfurt, Austria), Radim Bartos (University of New Hampshire, USA), Hermann Hellwagner (University of Klagenfurt, Austria)
  • Which CDN to Download From? A Client and Server Strategies
    Abdelhak Bentaleb (Concordia University, Canada), Reza Farahani (University of Klagenfurt, Austria), Farzad Tashtarian (University of Klagenfurt, Austria), Hermann Hellwagner (University of Klagenfurt, Austria), Roger Zimmermann (National University of Singapore, Singapore)

Titles: Modern Network-Assisted Delivery of Adaptive Video Streaming Services and Towards Sustainable Servessless Processing of Massive Graphs on Computing Continuum


During the session, experts delved into the challenges of processing massive amounts of data and explored cutting-edge technologies that can handle such extreme data requirements.

From graph-based solutions to distributed computing frameworks, attendees shared valuable insights into the evolving landscape of data management. The discussion highlighted the need for scalable infrastructure and intelligent algorithms to efficiently process and analyze vast datasets. The future of data management is promising, thanks to innovative approaches showcased in the session. Stay tued as we continue to push the boundaries of data processing and drive advancements in the field through the Graph-Massivizer Project Together, we’re shaping the future of extreme data management!

BDVA – Big Data Value Association