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: https://edge.itec.aau.at/.

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)
    https://dl.acm.org/doi/10.1145/3588444.3591044
  • 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)
    https://dl.acm.org/doi/10.1145/3588444.3591038
  • 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)
    https://dl.acm.org/doi/10.1145/3588444.3591030

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

Link: https://springschool.iaik.tugraz.at/

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

We are very happy to announce that Klaus Schoeffmann, with his lifelog retrieval system lifeXplore, has won the Lifelog Search Challenge 2023 (LSC2023). The LSC2023 was performed on June 12, 2023, in Thessaloniki, Greece, as a workshop at the ACM International Conference on Multimedia Retrieval (ICMR2023). In total, 30 different search tasks (known-item search, ad-hoc topic search, and question answering) had to be solved by all 14 international teams. After four hours of strong competition, lifeXplore came out on top of the other search systems and scored first.

Mathias Lux

Prejudice and discrimination against immigrants have been a constant presence in Western Europe for a long time, fueled considerably by the refugee crisis in 2015. This project, supervised by researchers from Game Studies & Engineering and Psychology, explores the possibilities of using virtual reality (VR)-based video games to increase empathy and positive attitudes toward the integration of refugees in our society. Virtual-reality games have enormous potential for immersing players emotionally in situations outside their usual experience and in the perspectives of others. The project involves the development and evaluation of a game in which participants go through typical experiences of refugees in a European country and an experimental study that assesses the effects of playing the game on empathy, perspective-taking, and implicit and explicit attitudes toward refugees. We believe that this approach has the potential to open up a new, attractive route for changing people’s attitudes through immersive virtual experiences.

The project was proposed by Judith Glück from the psychology department and Felix Schniz and Mathias Lux from ITEC and accepted for funding within the Ada Lovelace program of the University of Klagenfurt. The project will start in Q3 or Q4 2023 and has a duration of 4 years.

Our Graph-Massivizer Project is thrilled to be part of the #DataWeek2023 event! Join us for a thought-provoking session on “Are current infrastructures suitable for extreme data processing? Technologies for data management.”

Don’t miss this opportunity to explore cutting-edge solutions and discuss the future of data processing together with Nuria De Lama Dumitru Roman Roberta Turra Radu Prodan Lilit Axner Jan Martinovič Bill Patrowicz Irena Pavlova! ?

? Tuesday 13th

⏰ 15:30 – 17:00

BDVA – Big Data Value Association

 

Josef Hammer presented the paper “C3-Edge – An Automated Mininet-Compatible SDN Testbed On Raspberry Pis and Nvidia Jetsons” at the 36th IEEE/IFIP Network Operations and Management Symposium (NOMS 2023) in Miami, Florida, USA.

Authors: Josef Hammer, Dragi Kimovski, Narges Mehran, Radu Prodan, 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. While there is vast potential for research in these areas, managing complex adaptive infrastructure is difficult, and experiments with real hardware are tedious to set up. Furthermore, proposed solutions often require expensive hardware or labor-intensive procedures to replicate and build on these ideas. With our C3-Edge testbed, we address these challenges and propose a novel approach for automated edge testbed setup with a low-cost software-defined network and adaptive infrastructure configuration. We validated the efficiency of our approach on a real-world computing continuum infrastructure. The evaluation results confirm that our flexible approach is suitable for all but the most bandwidth-intensive applications.

Josef Hammer presented our work at NOMS 2023

For more information about the research, visit the website: https://edge.itec.aau.at/.

Container-based Data Pipelines on the Computing Continuum for Remote Patient Monitoring

Authors: Nikolay Nikolov, Arnor Solberg, Radu Prodan, Ahmet Soylu, Mihhail Matskin, Dumitru Roman

Computer Jounal, Special Issue on Computing in Telemedicine

Abstract: Diagnosing, treatment, and follow-up care of patients is happening increasingly through telemedicine, especially in remote areas where direct interaction is hindered. Over the past three years, following the COVID-19 pandemic, the utility of remote patient care has been further field-tested. Tackling the technical challenges of a growing demand for telemedicine requires a convergence of several fields: 1) software solutions for reliable, secure, and reusable data processing, 2) management of hardware resources (at scale) on the Cloud/Fog/Edge Computing Continuum, and 3) automation of DevOps processes for deployment of digital healthcare solutions with patients. In this context, the emerging concept of \emph{big data pipelines} provides relevant solutions and is one of the main enablers. In what follows, we present a data pipeline for remote patient monitoring and show a real-world example of how data pipelines help address the stringent requirements of telemedicine.