The Horizon Cloud Summit 2021 – at its second edition – aims to gather innovators and researchers, Cloud adopters, policymakers, and Cloud initiatives and open source projects to shape the EU digital transition.
Radu Prodan held an online presentation: “ARTICONF: A Cloud-agnostic Blockchain-as-a-Service for Social Continuum“ on December 09, 2021.
IEEE VCIP 2021
Sunday, December 5, 2021
https://www.vcip2021.org/call-for-tutorials/
Find further info in the blog post here.
ITEC is delighted to announce the next speaker in our guest lecture series – Prof. Dr. Michael Rielger from SIMULA Research, Norway. The course will take place from May 30 – June 03, 2022.
The course will give an introduction into the topic of generative adversarial networks and synthetic data. Please register at the course 623.714
Further information is available HERE.
We happily announce, the #Emmy Award Bitmovin (link) for innovation in online TV for @bitmovin arrived at University of Klagenfurt.
Alexander Lercher’s Master Thesis titled ‘Context-aware Community Evolution Prediction in Distributed Social Networks‘ under the supervision of Prof. Radu Prodan at ITEC has been awarded outstanding academic thesis and a grant of 600 Euros in the field of digitization for Master’s Category. This work received funding from European Union’s Horizon 2020 research and innovation programme, grant agreement 825134, “Smart Social Media Ecosystem in a Blockchain Federated Environment (ARTICONF)”. The award ceremony will take place on Wednesday, November 24th, 2021 at 11 a.m. in the mirror hall of the Carinthian Provincial Government.
Farzad Tashtarian is invited to talk on “LwTE: Light-weight Transcoding at the Edge” at IMDEA Networks Institute, Madrid, Spain.
You are a Master Student and want to get to know more about ATHENA in a 3 months ATHENA internship in 2022?
Come and join our team! Apply now.
(Please note: application deadline is 14 December 2021)
Title: FSpot: Fast Multi-Objective Heuristic for Efficient Video Encoding Workloads over AWS EC2 Spot Instance Fleet
Authors: Anatoliy Zabrovskiy, Prateek Agrawal, Vladislav Kashansky, Roland Kersche, Christian Timmerer, and Radu Prodan
Abstract: HTTP Adaptive Streaming (HAS) of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic. Video compression technology plays a vital role in efficiently utilizing network channels, but encoding videos into multiple representations with selected encoding parameters is a significant challenge. However, video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds. In turn, the public clouds, such as Amazon elastic compute cloud (EC2), provide hundreds of computing instances optimized for different purposes and clients’ budgets. Thus, there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations. Additionally, the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content. In this paper, we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple x264 codec encoding parameters and video sequences of varying complexity. Then, we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs. Furthermore, we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost. The results show that our approach, on average, can reduce the encoding costs by at least 15.8% and up to 47.8% when compared to a random selection of EC2 spot instances.
Keywords: EC2 Spot instance, Encoding time prediction; adaptive streaming; video transcoding; Clustering; HTTP adaptive streaming; MPEG-DASH; Cloud computing; optimization; Pareto front.
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:
Taichung, Taiwan, The 1st IEEE International Workshop on Data-Driven Rate Control for Media Streaming (DDRC’21) Co-located with the IEEE International Conference on Multimedia Big Data (BigMM’21)
Conference Website: https://www.bigmm.org/ (November 15-17)
HTTP Adaptive Streaming (HAS) — Quo Vadis?
Speaker: Professor Christian Timmerer
Time: November 16, 2021 12:10 (UTC +1)
CAdViSE or how to find the Sweet Spots of ABR Systems
Speaker: Babak Taraghi, M.Sc.
Time: November 16, 2021 13:00 (UTC +1)
Online attendance is free, Visit here for more information.