Auszeichnungen von ITEC Mitarbeitern

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Congratulations to Priv.-Doz. Dr. Dragi Kimovski: Completion of the Habilitation Procedure

On 03.02.2023 , Dragi Kimovski defended his habilitation thesis “The Computing Continuum in the Internet-of-Things Era: Beyond the Cloud Data Centers”. In the meantime, the procedure has been completed and we were happy to hand out the certificate. Congratulations!

Dragi Kimovski is a tenure track researcher at the Institute of Information Technology (ITEC), University of Klagenfurt. He earned his doctoral degree in 2013 from the Technical University in Sofia. He was an assistant professor at the University of Information Science and Technology in Ohrid and a senior researcher and lecturer at the University of Innsbruck. Kimovski conducted multiple research stays at renowned universities, including the University of Michigan, the University of Utrecht, the University of Bologna, and the University of Granada. He co-authored more than 60 articles in international conferences and journals. His research interests include parallel and distributed computing and multi-objective optimization for energy efficiency and sustainability. He acted as a scientific coordinator and work-package leader in dozen Horizon 2020 projects (DataCloud, ENTICE, and ASPIDE).

Fog and edge computing have been introduced as an extension of the cloud services towards the data sources, thus forming the computing continuum. The computing continuum enables the creation of a new type of services, spanning across distributed infrastructures, supporting various Internet of Things (IoT) applications. However, the introduction of the computing continuum raises multiple challenges for the management, deployment and orchestration of complex distributed applications, such as increased network heterogeneity, limited resource capacity of edge devices, fragmented storage management, high mobility of edge devices and limited support of native monolithic applications. Therefore, the habilitation thesis explores novel algorithms for low latency, scalable, and sustainable computing over heterogeneous resources for information processing and reasoning, thus enabling transparent integration of IoT applications. It tackles the heterogeneity challenge of dynamically changing computing infrastructure topologies and presents a novel concept for sustainable processing at scale.

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Hermann Hellwagner receives Carinthian cultural prize

Every year, Carinthia celebrates its cultural and scientific greats by awarding a total of 13 prizes based on the proposal of the Carinthian Cultural Board. This year Hermann Hellwagner received one of the three appreciation prizes in the natural and technical sciences category. Congratulations! Further information: https://www.aau.at/blog/kulturpreise-des-landes-kaernten-fuer-hermann-hellwagner-roswitha-rissner-und-wolfgang-puschnig/

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Hermann Hellwagner received appreciation award

We are happy to announce and our congratulations to Dr. Hermann Hellwagner for receiving the appreciation award of Carinthia in the area of natural/technical sciences.

 

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Narges Mehran got the student award to present the paper with the title of “Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum” at IEEE Cluster 2022.



Student travel award at IEEE Cluster 2022

Narges Mehran got the student award for presenting the paper titled “Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum” at IEEE Cluster 2022.

The presentation was on the 7th of September: https://clustercomp.org/2022/program/

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MPEG awarded a Technology & Engineering Emmy® Award for DASH

MPEG, specifically, ISO/IEC JTC 1/SC 29/WG 3 (MPEG Systems), has been just awarded a Technology & Engineering Emmy® Award for its ground-breaking MPEG-DASH standard. Dynamic Adaptive Streaming over HTTP (DASH) is the first international de-jure standard that enables efficient streaming of video over the Internet and it has changed the entire video streaming industry including — but not limited to —  on-demand, live, and low latency streaming and even for 5G and the next generation of hybrid broadcast-broadband. The first edition has been published in April 2012 and MPEG is currently working towards publishing the 5th edition demonstrating an active and lively ecosystem still being further developed and improved to address requirements and challenges for modern media transport applications and services.

This award belongs to 90+ researchers and engineers from around 60 companies all around the world who participated in the development of the MPEG-DASH standard for over 12 years.

From left to right: Kyung-mo Park, Cyril Concolato, Thomas Stockhammer, Yuriy Reznik, Alex Giladi, Mike Dolan, Iraj Sodagar, Ali Begen, Christian Timmerer, Gary Sullivan, Per Fröjdh, Young-Kwon Lim, Ye-Kui Wang. (Photo © Yuriy Reznik)

Christian Timmerer, director of the Christian Doppler Laboratory ATHENA, chaired the evaluation of responses to the call for proposals and since that served as MPEG-DASH Ad-hoc Group (AHG) / Break-out Group (BoG) co-chair as well as co-editor for Part 2 of the standard. For a more detailed history of the MPEG-DASH standard, the interested reader is referred to Christian Timmerer’s blog post “HTTP Streaming of MPEG Media” (capturing the development of the first edition) and Nicolas Weill’s blog post “MPEG-DASH: The ABR Esperanto” (DASH timeline).

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MPEG DASH video streaming technology co-developed in Klagenfurt wins Technology & Engineering Emmy® Award

The Emmy® Awards do not only honour the work of actors and directors, but they also recognise technologies that are steadily improving the viewing experience for consumers. This year, the winners include the MPEG DASH Standard. Christian Timmerer (Department of Information Technology) played a leading role in its development. Read more about it here.

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ANGELA won the 2nd Best Paper Award in IFIP/IEEE PEMWN 2021 Conference

The ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator paper from ATHENA CD laboratory has won the 2nd Best Paper Award in the 10th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN).

More information about the paper can be found in the blog post.

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Emmy Award Bitmovin for Innovation at University Klagenfurt

We happily announce, the #Emmy Award Bitmovin (link) for innovation in online TV for @bitmovin arrived at University of Klagenfurt.

 

 

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FaRes-ML Won the Best New Streaming Innovation Award in the Streaming Media Readers’ Choice Awards 2021

The Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning paper from ATHENA lab has won the Best New Streaming Innovation Award in the Streaming Media Readers’ Choice Awards 2021.

The journey that led to the publication of the FaRes-ML paper was quite an insightful one.

It all started with the question, “How to efficiently provide multi-rate representations over a wide range of resolutions for HTTP Adaptive Streaming?“. This led to the first publication, Fast Multi-Rate Encoding for Adaptive HTTP Streaming, in which we proposed a double-bound approach to speed up the multi-rate encoding. After analyzing the results, we saw room for improvement in parallel encoding performance, which led to the second publication Towards Optimal Multirate Encoding for HTTP Adaptive Streaming. The results were promising, but we believed we could improve the encoding performance by utilizing machine learning. That was the primary motivation behind our third paper, FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning. In FaMe-ML, we have used convolutional neural networks (CNNs) to use the information from the reference representation better to encode other representations, resulting in significant improvement in the multi-rate encoding performance. Finally, we proposed FaRes-ML to extend our FaME-ML approach to include multi-resolution scenarios in Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning paper.

Here is the list of publications that led to FaRes-ML:

  1. Fast Multi-Rate Encoding for Adaptive HTTP Streaming. Published in DCC’20.
  2. Towards Optimal Multirate Encoding for HTTP Adaptive Streaming. Published in MMM’21.
  3. FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Learning. Published in VCIP’20.
  4. Fast Multi-Resolution and Multi-Rate Encoding for HTTP Adaptive Streaming Using Machine Learning. Published in IEEE OJ-SP.